Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 CONTENT RECOMMENDATION SYSTEM BASED ON PRIVATE DYNAMIC USER PROFILE TING CHEN, WEI-LI HAN, HAI-DONG WANG, YI-XUN ZHOU, BIN XU, BIN-YU ZANG Software School. Fudan University, China E-MAIL:{052053009,whan,062053008,0461057,0361069,byzang;@fudan.edu.cn Abstract: is a classification tool representing their preferences As the amount of the accessible information in the Content Recommendation(CR)technology [15] can Internet is overwhelming, personalized content help users get preferred information according to the users recommendation system offers spam filtering service and suggests useful information to the end users. It is a hotspot in predefined preferences 14] or some usage patterns mined the research area of content management on www from web access logs [4]. In general, more detailed priva Traditional recommendation systems do the data mining on user information, including personal schedules, emails, web access logs, discover user's access patterns, and filter the recently visited websites, can help CR system to provide information on behalf of the user at the server side. One more accurate contents. However, this may cause the critical limitation of traditional recommendation system is the privacy leak issue lack of users private daily data, such as schedules, favorite In traditional content providing websites and personal emails. The reason for this limitation is personal data to improve the accuracy of the provided the privacy leak issue when the server holds much more information only include web access logs, predefined static private user data. To solve this problem, this paper presents preference lists, part of the contact information. Users do Content REcommendation System based on private Dynamic server Besid not want to store much detailed personal information on the server. Besides, users must set their preference lists and updates private Dynamic User Profile(DUP) at the client side. service from different service pronti set the customized private data of user at the client side, discover, stores and contact information on each server to The system fetches preferred message from the content server In this paper, we present a novel agent-based according to DUP. An important usage of this technology is a personalized recommendation system called Content personalized advertising system in the rss(rich Site REcommendation System based on private Dynamic User Summary, or RDF Site Summary) reader application. Our Profile (CRESDUP).Thi ble to find out experiment shows that the system can utilize DUP to identif he customers' potential preferences and deliver the more preferred messages on the Internet according to private preferred messages, especially the advertisements, to people data and protect the privacy of user at the same time who are interested these personal data are processed at the client side. A private Dynamic User Profile(DuP) is constructed through Keywords data mining on user's personal data at the client side DUP Content recommendation; Dynamic user profile; is regularly updated according to the changes of the Privacy-Enhanced personalization; Data mining: RSs personal data during users daily operations and the user's feedbacks. Furthermore. DUP can be reused for different 1. Introduction content service providers because it is stored and utilized at the client side Nowadays as the information world is increasingly The rest of the paper lized as follows Section 2 panding, the problem of the overwhelming information introduces the background and surveys the related work flood is becoming more and more serious [6]. Users are Section 3 presents our personalized CR method; Section 4 overloaded by thousands of messages and a large quantity describes an application scenario: RSS Ad service with of information, most of which are spam [9]. What they need cresdup discusses some main issues related to our 1-4244-0973-X07/$25.00@2007|EEE 2112
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 CONTENT RECOMMENDATION SYSTEM BASED ON PRIVATE DYNAMIC USER PROFILE TING CHEN, WEI-LI HAN, HAI-DONG WANG, YI-XUN ZHOU, BIN XU, BIN-YU ZANG Software School, Fudan University, China E-MAIL: {052053009*, wlhan, 062053008, 0461057, 0361069, byzang}@fudan.edu.cn Abstract: As the amount of the accessible information in the Internet is overwhelming, personalized content recommendation system offers spam filtering service and suggests useful information to the end users. It is a hotspot in the research area of content management on WWW. Traditional recommendation systems do the data mining on web access logs, discover user’s access patterns, and filter the information on behalf of the user at the server side. One critical limitation of traditional recommendation system is the lack of user’s private daily data, such as schedules, favorite websites and personal emails. The reason for this limitation is the privacy leak issue when the server holds much more private user data. To solve this problem, this paper presents an agent-based personalized recommendation method called Content REcommendation System based on private Dynamic User Profile (CRESDUP). The system collects and mines the private data of user at the client side, discovers, stores and updates private Dynamic User Profile (DUP) at the client side. The system fetches preferred message from the content server according to DUP. An important usage of this technology is a personalized advertising system in the RSS (Rich Site Summary, or RDF Site Summary) reader application. Our experiment shows that the system can utilize DUP to identify the customers’ potential preferences and deliver the more preferred messages, especially the advertisements, to people who are interested. Keywords: Content recommendation; Dynamic user profile; Privacy-Enhanced personalization; Data mining; RSS 1. Introduction Nowadays as the information world is increasingly expanding, the problem of the overwhelming information flood is becoming more and more serious [6]. Users are overloaded by thousands of messages and a large quantity of information, most of which are spam [9]. What they need is a classification tool representing their preferences. Content Recommendation (CR) technology [15] can help users get preferred information according to the users’ predefined preferences [14] or some usage patterns mined from web access logs [4]. In general, more detailed private user information, including personal schedules, emails, recently visited websites, can help CR system to provide more accurate contents. However, this may cause the privacy leak issue. In traditional content providing services [16], the personal data to improve the accuracy of the provided information only include web access logs, predefined static preference lists, part of the contact information. Users do not want to store much detailed personal information on the server. Besides, users must set their preference lists and contact information on each server to get the customized service from different service providers. In this paper, we present a novel agent-based personalized recommendation system called Content REcommendation System based on private Dynamic User Profile (CRESDUP). This system is able to find out preferred messages on the Internet according to private user data and protect the privacy of user at the same time as these personal data are processed at the client side. A private Dynamic User Profile (DUP) is constructed through data mining on user’s personal data at the client side. DUP is regularly updated according to the changes of the personal data during user’s daily operations and the user’s feedbacks. Furthermore, DUP can be reused for different content service providers because it is stored and utilized at the client side. The rest of the paper is organized as follows. Section 2 introduces the background and surveys the related work; Section 3 presents our personalized CR method; Section 4 describes an application scenario: RSS Ad service with CRESDUP, discusses some main issues related to our 1-4244-0973-X/07/$25.00 ©2007 IEEE 2112
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 method in the scenario and evaluates its performance; 2.3. Motivation Section 5 discusses some remaining issues, Section 6 concludes the paper and discusses the future work This paper provides a novel method to recommend preferred message to end user based on his/her private data We apply this method to advertisement recommendation during RSS content pushing 2. 1. Content Recommendation 2. 4. Related works To provide more personalized service on web, Web Usage Mining(WUM) is proposed [16]. WUM typically The content-based approach of recommendation has extracts knowledge by analyzing historical data of users or its roots in information retrieval [11, and information servers. Web Personalized and recommender systems filtering [3] research. Content-based systems are designed [16-17] are typical applications of WUM mostly to recommend text-based items; the content in these Recommendation systems emerged as an independent systems is usually described with key words. For example, a research area in the mid-1990s and are usually classified content-based component of the Fab system [2], which into three categories, based on how recommendations are recommends web pages to users, represents web page made [21 content with the 100 most important words. Similarly, the Content-based recommendations: The user will be Syskill Webert system [8] represents documents with the recommended items similar to the ones the user 128 most informative words preferred in the past Besides the traditional heuristics that are based mostly Collaborative recommendations: The user will be on information retrieval methods, other techniques for recommended items that people with similar content-based recommendation have also been used, such tastes and preferences liked in the past as Bayesian classifiers [7, and various machine learning Hybrid approaches: These methods combine techniques, including clustering, decision trees, and content-based and collaborative method artificial neural networks [8]. In the area of user profile CRESDUP focuses primarily on content-based processing and evolution, Sparacino [13] presents a method recommendation methods since it need not communicate based on Bayesian networks to construct a profile for with other users providing customized services to museum visitors 2.2. Content Pushing Service and Anti-Spam Architecture of crest Content Pushing Service(CPS)can deliver subscribed 3.1. Overview messages to user. RSS is a popular application of CPS. To use RSS, users subscribe to sites and monitor the updates of To classify the messages according to the users these sites with an RSS reader. The rSS reader retrieves the preferences, we have designed an intelligent personalized RSS feed periodically from a server by downloading the content recommendation system. We name this system RSS data file CRESDUP(Content REcommendation System based However, RSS may push some messages which are ne preferred by subscriber. The preferences of subscriber are private Dynamic User Profile). In figure 1, the three-tier of dynamically changing, but the subscribed feeds do not the system is demonstrated. Details will be discussed in the change so fast to fit hIs/ner preferences. If these following sections uninteresting messages are numerous, the subscriber may There are four core modules in our system refuse to use the CPs. This problem does harm to content User Raw Information Collection Agent; User Profile providers, because these messages could bind with some Analysis Agent; Content Recommendation Server Content advertisements, which are the main source of income for Recommendation Client Agent most content providers. This is a typical security issue The system consists of three tiers. The first tier is the anti-spam [10-12 data layer, including the user raw information collection agent. The second tier is the logic layer, including the build-up process of DUP and the usage of DUP through 2113
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 method in the scenario and evaluates its performance; Section 5 discusses some remaining issues; Section 6 concludes the paper and discusses the future work. 2. Background 2.1. Content Recommendation To provide more personalized service on web, Web Usage Mining (WUM) is proposed [16]. WUM typically extracts knowledge by analyzing historical data of users or servers. Web Personalized and recommender systems [16-17] are typical applications of WUM. Recommendation systems emerged as an independent research area in the mid-1990s and are usually classified into three categories, based on how recommendations are made [2]. z Content-based recommendations: The user will be recommended items similar to the ones the user preferred in the past; z Collaborative recommendations: The user will be recommended items that people with similar tastes and preferences liked in the past. z Hybrid approaches: These methods combine content-based and collaborative methods. CRESDUP focuses primarily on content-based recommendation methods since it need not communicate with other users. 2.2. Content Pushing Service and Anti-Spam Content Pushing Service (CPS) can deliver subscribed messages to user. RSS is a popular application of CPS. To use RSS, users subscribe to sites and monitor the updates of these sites with an RSS reader. The RSS reader retrieves the RSS feed periodically from a server by downloading the RSS data file. However, RSS may push some messages which are not preferred by subscriber. The preferences of subscriber are dynamically changing, but the subscribed feeds do not change so fast to fit his/her preferences. If these uninteresting messages are numerous, the subscriber may refuse to use the CPS. This problem does harm to content providers, because these messages could bind with some advertisements, which are the main source of income for most content providers. This is a typical security issue: anti-spam [10-12]. 2.3. Motivation This paper provides a novel method to recommend preferred message to end user based on his/her private data. We apply this method to advertisement recommendation during RSS content pushing. 2.4. Related Works The content-based approach of recommendation has its roots in information retrieval [1], and information filtering [3] research. Content-based systems are designed mostly to recommend text-based items; the content in these systems is usually described with keywords. For example, a content-based component of the Fab system [2], which recommends web pages to users, represents web page content with the 100 most important words. Similarly, the Syskill & Webert system [8] represents documents with the 128 most informative words. Besides the traditional heuristics that are based mostly on information retrieval methods, other techniques for content-based recommendation have also been used, such as Bayesian classifiers [7], and various machine learning techniques, including clustering, decision trees, and artificial neural networks [8]. In the area of user profile processing and evolution, Sparacino [13] presents a method based on Bayesian networks to construct a profile for providing customized services to museum visitors. 3. Architecture of Cresdup 3.1. Overview To classify the messages according to the user’s preferences, we have designed an intelligent personalized content recommendation system. We name this system CRESDUP (Content REcommendation System based on private Dynamic User Profile). In figure 1, the three-tier of the system is demonstrated. Details will be discussed in the following sections. There are four core modules in our system: User Raw Information Collection Agent; User Profile Analysis Agent; Content Recommendation Server; Content Recommendation Client Agent. The system consists of three tiers. The first tier is the data layer, including the user raw information collection agent. The second tier is the logic layer, including the build-up process of DUP and the usage of DUP through 2113
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 different agents. The third tier is the presentation layer, Because of the different formats of the raw data, they including the customized Ul (User Interface)on the pc are formatted into predefined XML files clients or mobile devices 3.3. User Profile Analysis Agent he user profile analysis agent makes a user profile from the formatted raw data. In this system, content-based ser Raw lnformation Collection Age recommendation algorithm is employed. The user will be recommended the data items similar to the ones they preferred in the past In content-based recommendation methods [2], utilities(c, d), which is the importance of a data item d for Cser Profile Analysis Ageat user c is estimated from the known utilities(c, dv assigned by user c to data items d, ED which are similar to data item d After the raw information data are collected and detent formatted into predefined XML files. the user profile analysis agent uses the term frequency/inverse document frequency(TF-IDF)[5]measure for specifying the key word weights of the raw data. A user profile, i.e., a set of PDAHandhy phones attributes characterizing item, is made and used to determine the appropriateness of the information item for Figure 1. The CResdUP system as a three-tier application TF-IDF is defined as follows: Assume that n is the total number of documents that are collected and that 3. 2. User Raw Information Collection Agent keyword k, appears in n, of them.Moreover,assume The user profile includes user's work pattern and life that fi, is the number of times keyword k, appears in mode. It is created from the user raw information data. An document Then TFi, the term frequency of keyword agent is employed to do the data collection work. It collects the raw data from the user's personal data resources locally k, in document,, is defined Basic Personal Information: Name, Age, Gender TF.= Location, Company, Preference Lists, etc max. J. Recently Visited URLs: The websites the user has (1) browsed where the maximum is computed over the frequencies Self-made documents: The documents which are of all keywords k, that appear in the document d designed, written and reviewed by the includer Since keywords that appear in many documents are not emails. notes and work documents distinguishing a relevant document from an Calendar and Schedule: The schedule of a user. both irrelevant one. Therefore, the measure of inverse document hat of work and life frequency (IDE) is often used in combination with simple Favorite Websites: The websites user adds to the term frequency (TE ) The inverse document frequency for favorite list of the browse A feedback agent is used to collect the feedbacks as keyword k, is usually defined as important source for the improvement of DUP. So that DUP can be updated regularly and represent the user's IDF =log dynamically-changing preferences 2114
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 different agents. The third tier is the presentation layer, including the customized UI (User Interface) on the PC clients or mobile devices. User Raw Information Collection Agent Unified XML File Unified XML File Unified XML File Personal Information My Documents Favorite Websites User Profile Analysis Agent Personalized User Profile Content Recommendation Client Agent(on PC , PDA, or Smart Phone) Content Recommendation Server Recently Visited URLs Calendar Data (Extraction) Service Platform(Brower, PDA,Handy phones...) Logic(Analysis) Presentation Figure 1. The CRESDUP system as a three-tier application 3.2. User Raw Information Collection Agent The user profile includes user’s work pattern and life mode. It is created from the user raw information data. An agent is employed to do the data collection work. It collects the raw data from the user’s personal data resources locally which include: Basic Personal Information: Name, Age, Gender, Location, Company, Preference Lists, etc. Recently Visited URLs: The websites the user has browsed recently. Self-made documents: The documents which are designed, written and reviewed by the user, including emails, notes and work documents. Calendar and Schedule: The schedule of a user, both that of work and life. Favorite Websites: The websites user adds to the favorite list of the browser. A feedback agent is used to collect the feedbacks as an important source for the improvement of DUP. So that DUP can be updated regularly and represent the user’s dynamically-changing preferences. Because of the different formats of the raw data, they are formatted into predefined XML files. 3.3. User Profile Analysis Agent The user profile analysis agent makes a user profile from the formatted raw data. In this system, content-based recommendation algorithm is employed. The user will be recommended the data items similar to the ones they preferred in the past. In content-based recommendation methods [2], utilities(c, d), which is the importance of a data item d for user c is estimated from the known utilities(c, di) assigned by user c to data items di∈D which are similar to data item d. After the raw information data are collected and formatted into predefined XML files, the user profile analysis agent uses the term frequency/inverse document frequency (TF-IDF) [5] measure for specifying the keyword weights of the raw data. A user profile, i.e., a set of attributes characterizing item, is made and used to determine the appropriateness of the information item for recommendation purposes. TF-IDF is defined as follows: Assume that is the total number of documents that are collected and that keyword appears in of them. Moreover, assume that is the number of times keyword appears in document . Then , the term frequency of keyword in document , is defined as N j k i n i j f , i k j d TFi, j i k d j z z j i j i j f f TF , , , max = (1) where the maximum is computed over the frequencies of all keywords that appear in the document . Since keywords that appear in many documents are not useful in distinguishing a relevant document from an irrelevant one. Therefore, the measure of inverse document frequency ( ) is often used in combination with simple term frequency ( ). The inverse document frequency for keyword is usually defined as z j f , z k d j i IDF TFi, j i k i i n N IDF = log (2) 2114
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 Then, the TF-IDF weight for keyword k, in TF-IDF vectors w and w of keyword weights,the document d. is defined as relation between them can be evaluated by some scoring =TE×IDF (3) heuristic, such as the cosine similarity measure And the content of document d is defined as uI(C, Il cos(ww)=cl content(d4)=(1…w) A document often contains different source types of keywords, such as subjects, contents, and other types of ∑wl"n attributes. Hence, the final profile of the document is made up created from weighted keywords lists by the normalization of weighted keywords. Finally, the dUP is a collection of all the document profiles where K is the total number of keywords in the system. Consequently, the algorithm will assign higher 3. 4. Content Recommendation Server values to those contents that have more similar features in the metadata file as dup Then the client selects these The content recommendation server manages the contents and sends them to the client according to the client contents, creates a content request and sends it to the request. The content recommendation server organizes the be delivered to the client by the server non-leaf node of the tree is a category while the leaf node is 3.6. Main Workflow of CRESDUP a piece of content. By TF-IDF algorithm, the keywords are extracted from the raw contents and the content profiles for each category are maintained -- Movies 回1回 Figure 2. Category-tree on the server Figure 3. Workflow of CRESDUP system The content profiles for each category will be used as the metadata of the contents It is formatted into Xml files sequence is illustrated in figure 3 With this metadata, the server does not have to send the Firstly, the information collection agent collects the whole content to the client for the selection and filtering data from the user's daily usages on the computer. Secondly, process. Thus, it saves time and network resource with the user profile analysis agent, a DUP representing user's preferences is produced from these data. Thirdly, the 3.5. Content Recommendation Client Agent content recommendation client agent uses dup to select The client is an agent to receive information data from recommendation server. The client can be deployed on the the server based on users profile. It can either be an desktop PC/laptop PC, smart phones, PDAs and other smart independent module of the client-side software or an client devices. The whole working process is conducted on integrated part of the client-side software By receiving the content metadata profile from the the client side with no interference from public servers server, the agent can compare it with the local DUP. Since both content metadata profile and DUP are represented as 2115
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 Then, the TF-IDF weight for keyword in document is defined as i k j d i j i j i w, = TF, × IDF (3) And the content of document is defined as d j ( ) ( ... ) d j w1 j, ,wkj content = A document often contains different source types of keywords, such as subjects, contents, and other types of attributes. Hence, the final profile of the document is made up created from weighted keywords lists by the normalization of weighted keywords. Finally, the DUP is a collection of all the document profiles. 3.4. Content Recommendation Server The content recommendation server manages the contents and sends them to the client according to the client request. The content recommendation server organizes the to-be-pushed contents into a category-tree (Figure 2). Each non-leaf node of the tree is a category while the leaf node is a piece of content. By TF-IDF algorithm, the keywords are extracted from the raw contents and the content profiles for each category are maintained. Hotels Restaurants Concerts Movies A B C D E F G H I K L Arts M N O B-1 B-2 B-3 N-1 N-2 N-3 J Figure 2. Category-tree on the server The content profiles for each category will be used as the metadata of the contents. It is formatted into XML files. With this metadata, the server does not have to send the whole content to the client for the selection and filtering process. Thus, it saves time and network resource. 3.5. Content Recommendation Client Agent The client is an agent to receive information data from the server based on user’s profile. It can either be an independent module of the client-side software or an integrated part of the client-side software. By receiving the content metadata profile from the server, the agent can compare it with the local DUP. Since both content metadata profile and DUP are represented as TF-IDF vectors and of keyword weights, the relation between them can be evaluated by some scoring heuristic, such as the cosine similarity measure. wc wu ∑ ∑ ∑ = = = = × ⋅ = = K i i u K i i c K i i c i u c u c u c u w w w w w w w w u c u w w 1 2 , 1 2 , 1 , , 2 2 , ( , ) cos( ) G G G G G G (4) where K is the total number of keywords in the system. Consequently, the algorithm will assign higher values to those contents that have more similar features in the metadata file as DUP. Then the client selects these contents, creates a content request and sends it to the content recommendation server. The preferred contents will be delivered to the client by the server. 3.6. Main Workflow of CRESDUP User Raw Information Collection Agent User Profile Analysis Agent Content Recommendation Client Content Recommendation Server Collect Raw Data Start Analysis Start Recommendation Ask for content metadata Reply with content metadata Send selected contents request Reply with contents selected Update the DUP Figure 3. Workflow of CRESDUP system The main workflow sequence is illustrated in figure 3. Firstly, the information collection agent collects the data from the user’s daily usages on the computer. Secondly, with the user profile analysis agent, a DUP representing user’s preferences is produced from these data. Thirdly, the content recommendation client agent uses DUP to select and filter the information received from the content recommendation server. The client can be deployed on the desktop PC/laptop PC, smart phones, PDAs and other smart client devices. The whole working process is conducted on the client side with no interference from public servers. 2115
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 4. Application Scenario and Performance Evaluation 4.1. Application Scenario Description The RSS-based advertising system presented in this Ad Database paper is an application of CRESDUP and is used to demonstrate the features of the technologies mentioned above dvertising on Rss readers based on users preferences has large potential due to the very personal and Figure 5. Main architecture and work flow sequence of this atimate nature of the RSS application. The subscribed Rss RSS advertising system other personal information provides a precise personal The main architecture and work flow sequence of this profile of the user. Offering valuable and related ads when RSS advertising system are illustrated in Figure 5 he user is reading the rss data greatly improves the ad (1) User starts the rss reader and subscribes some effectiveness. The advertising system is augmented with RSS feeds personalized profiles so that only relevant, targeted ads are (2) The user raw information collection agent and the pushed to the users user profile analysis agent work together to build The system is divided into two parts: an Rss reade DUP Figure 4)with CRESDUP support and an advertisement (3) The Rss reader sends the ad category metadata rer request to the ad server and receives the reply The RSS reader is similar to the traditional RSS reader (4) The client agent analyses the category metadata except for its advertising function. It contains an ad bar and computes the similarity between each node of below the rss view window. The ad contents provided in the category tree and DUP this banner are carefully chosen with respect to DUP (5) The compared results indicate the user's potential Therefore, the user is able to get the most interesting nterests of the ads in this category, the higher the dvertisements better. Depending on this result, the ads server he ad server manages the ads, classifies them into sends the corresponding ads to the rss reader organized categories and sends the ads to the ad agent clients according to their requests (6)When the user receives and reads the ads,an plicit feedback is made so that the system could also learn users preferences by giving option what's hew Exchusive-Watch exdusive cps and the HD like"I need more" and"No thanks". The dup is It is worth noting that the user's preferences are kept on the client side instead of the server. Thus the privacy is Sometimette, protected from being exposed to the server mysterpunty deserted. Ater spotting a sw n a start mountain 4.2. Performance evaluation In order to validate our application, several experiments have been performed. The ad server contains Icket 200 pieces of ads divided into 10 categories, including movies,music,fashion, automobile, toys, sport, shops, jobs health care and PC games. The DUP raw data resources include the user ' s subscribed rss feeds favorite websites Figure 4. The Client UI and daily schedules. The weights of these resources can be hanged and the sum of all the weights is 1.0. In the 2116
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 4. Application Scenario and Performance Evaluation 4.1. Application Scenario Description The RSS-based advertising system presented in this paper is an application of CRESDUP and is used to demonstrate the features of the technologies mentioned above. Advertising on RSS readers based on user’s preferences has large potential due to the very personal and intimate nature of the RSS application. The subscribed RSS feeds imply the interests of the user. Combining them with other personal information provides a precise personal profile of the user. Offering valuable and related ads when the user is reading the RSS data greatly improves the ad effectiveness. The advertising system is augmented with personalized profiles so that only relevant, targeted ads are pushed to the users. The system is divided into two parts: an RSS reader (Figure 4) with CRESDUP support and an advertisement server. The RSS reader is similar to the traditional RSS reader except for its advertising function. It contains an ad bar below the RSS view window. The ad contents provided in this banner are carefully chosen with respect to DUP. Therefore, the user is able to get the most interesting advertisements. The ad server manages the ads, classifies them into organized categories and sends the ads to the ad agent clients according to their requests. Figure 4. The Client UI Step 1 and Step 2 Step 3 Ad Server Ad Database Step 5 Step 4 User Profile Step 6 Figure 5. Main architecture and work flow sequence of this RSS advertising system The main architecture and work flow sequence of this RSS advertising system are illustrated in Figure 5: (1) User starts the RSS reader and subscribes some RSS feeds. (2) The user raw information collection agent and the user profile analysis agent work together to build DUP. (3) The RSS reader sends the ad category metadata request to the ad server and receives the reply. (4) The client agent analyses the category metadata and computes the similarity between each node of the category tree and DUP. (5) The compared results indicate the user’s potential interests of the ads in this category, the higher the better. Depending on this result, the ads server sends the corresponding ads to the RSS reader client. (6) When the user receives and reads the ads, an explicit feedback is made so that the system could also learn user’s preferences by giving options like “I need more” and “No, thanks”. The DUP is dynamically updated. It is worth noting that the user’s preferences are kept on the client side instead of the server. Thus the privacy is protected from being exposed to the server. 4.2. Performance Evaluation In order to validate our application, several experiments have been performed. The ad server contains 200 pieces of ads divided into 10 categories, including movies, music, fashion, automobile, toys, sport, shops, jobs, health care and PC games. The DUP raw data resources include the user’s subscribed RSS feeds, favorite websites and daily schedules. The weights of these resources can be changed and the sum of all the weights is 1.0. In the 2116
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 experiment, the weight of favorite websites is modified transparent and effective, which makes the user's life easy from o to 1.0 and the other two resources share the This recommendation system can be extended in remaining weight value equally several ways, which include improving the understanding The first experiment shown in Figure 6a is the of users and contents, incorporating the contextual evaluation of the recommendation quality. The users are information into the recommendation process, supporting asked to give a score to the ads. The higher score indicates multiple ratings, and providing more flexible and less the more useful the ads are. It can be seen that most of the intrusive types of recommendations ads selected by CrEsdUP meet the users tastes. We are planning to make this system take additional Figure 6b is the cost time of processing client requests contextual information, such as time, place, and the job of on the server The overhead introduced is small. Therefore. the user into consideration when recommending the related CRESDUP is efficient information -Rs References [1 Baeza-Yates, R, Ribeiro-Beto, B. Modern Information Retrieval. Addison-Wesley. (1999) [2] Balabanovic, M, Shoham, Y: Fab: Content-Based, Collaborative Recommendation. Comm. ACM. vol 40. no. 3 (1997)66-72. 3 Belkin, N, Croft, B. Information Filtering and Information Retrieval Comm. ACM, vol 35, no 12,(1992)29-37 (a) Quality of Recommendation [4] Ron, K: Mining e-commerce data: the good, the bad, and ng Client Requests the ugly. Proceedings of the seventh ACM SIGKDD international conference(2001) 5 Jing, L, Huang, H, Shi, H: Improved Feature Selection Approach TFIDF in Text Mining, Proc. I Internet 6 Ali, F F. and Don, H.D. Managerial information overload. Comm.ACM,vol.45,no.10,(2002)127-131 [7 Mooney, RJ., Bennett, P.N., Roy, L: Book Recommending Using Text Categorization with Extracted Information. Proc. Recommender Systems Papers from 1998 Workshop, (b) Time Cost of Recommendation Technical Report WS-98-08.(1998) Figure 6. The performance evaluation 8 Pazzani, M, Billsus, D: Learning and Revising User Profiles: The Identification of Interesting Web Sites 5. Discussion Machine learning, vol, 27, (1997)313-331 9 Thede, L, Marshall, V.A., Rick W: An Economic Answer to namic updating of DuP is a remaining issue in Unsolicited Communication. EC04.(2004) CRESDUP. We use a simple offline solution to update DUp [10] Goodman, J and Rounthwaite, R: Stopping outgoing spam In Proceedings of the ACM Conference on Electronic A new DUP is periodically created. If Commerce(EC04)(New York, May 17-20), ACM Press, creating DUP is critical, the online way to update DUp is a better option. It can reduce the updating cost of DUP. [11 Sahami, M, Dumais, S, Heckerman, D, and Horvitz, E: A Bayesian approach to Filtering Junk e-mail. In Learning for 6. Conclusion and Future work Text Categorization- Papers form the AAAl Workshop AAAI Technical Report WS-98-05(Madison, W, 1998) Based on personalized content recommendation [12] Goodman, J, Cormack, G V,Hecherman, D: Spam and the service. the information classification and filtering are done Ongoing Battle for the Inbox, Comm. ACM, Vol 50, 2(Feb The 2007):25-33 [13 Sparacino, F: Sto(ry )chastics: A Bayesian Network 2117
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 experiment, the weight of favorite websites is modified from 0 to 1.0 and the other two resources share the remaining weight value equally. The first experiment shown in Figure 6a is the evaluation of the recommendation quality. The users are asked to give a score to the ads. The higher score indicates the more useful the ads are. It can be seen that most of the ads selected by CRESDUP meet the user’s tastes. Figure 6b is the cost time of processing client requests on the server. The overhead introduced is small. Therefore, CRESDUP is efficient. (a) Quality of Recommendation (b) Time Cost of Recommendation Figure 6. The Performance Evaluation 5. Discussion Dynamic updating of DUP is a remaining issue in CRESDUP. We use a simple offline solution to update DUP. A new DUP is periodically created. If the cost time of creating DUP is critical, the online way to update DUP is a better option. It can reduce the updating cost of DUP. 6. Conclusion and Future Work Based on personalized content recommendation service, the information classification and filtering are done automatically. The intelligent information process is transparent and effective, which makes the user’s life easy. This recommendation system can be extended in several ways, which include improving the understanding of users and contents, incorporating the contextual information into the recommendation process, supporting multiple ratings, and providing more flexible and less intrusive types of recommendations. We are planning to make this system take additional contextual information, such as time, place, and the job of the user, into consideration when recommending the related information. References [1] Baeza-Yates, R., Ribeiro-Beto, B.: Modern Information Retrieval. Addison-Wesley. (1999) [2] Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Comm. ACM, vol. 40, no. 3, (1997) 66-72. [3] Belkin, N., Croft, B.: Information Filtering and Information Retrieval. Comm. ACM, vol. 35, no. 12, (1992) 29-37 [4] Ron, K.: Mining e-commerce data: the good, the bad, and the ugly. Proceedings of the seventh ACM SIGKDD international conference(2001) [5] Jing, L., Huang, H., Shi, H.: Improved Feature Selection Approach TFIDF in Text Mining, Proc. 1st Internet Conference on Machine Learning and Cybernetics, Beijing (2002) [6] Ali, F.F. and Don, H.D.: Managerial information overload. Comm. ACM, vol. 45, no. 10, (2002) 127 - 131 [7] Mooney, R.J., Bennett, P.N., Roy, L.: Book Recommending Using Text Categorization with Extracted Information. Proc. Recommender Systems Papers from 1998 Workshop, Technical Report WS-98-08. (1998) [8] Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning, vol.27, (1997) 313-331 [9] Thede, L.,Marshall, V.A.,Rick W.:An Economic Answer to Unsolicited Communication. EC’04. (2004) [10] Goodman, J. and Rounthwaite, R.: Stopping outgoing spam. In Proceedings of the ACM Conference on Electronic Commerce (EC’04) (New York, May 17-20), ACM Press, New York, 2004, 30-39. [11] Sahami, M., Dumais, S., Heckerman, D., and Horvitz, E.: A Bayesian approach to Filtering Junk e-mail. In Learning for Text Categorization – Papers form the AAAI Workshop. AAAI Technical Report WS-98-05 (Madison, WI, 1998). [12] Goodman, J., Cormack, G. V., Hecherman, D.: Spam and the Ongoing Battle for the Inbox, Comm. ACM, Vol 50, 2 (Feb., 2007): 25-33. [13] Sparacino, F.: Sto(ry)chastics: A Bayesian Network 2117
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 Architecture for User Modeling and Computational Computing Systems, 1995 Storytelling for Interactive Spaces. In Proceedings of [16] Baraglia R, Silvestri F: Dynamic Personalization of Web UBicomp 2003, Seattle, WA. USA, Springer.(2003) Sites without User Intervention. Comm. ACM. Vol. 50. 2 [14 Miller, B N, Albert, I, Lam, S.K., Konstan, J.A. and riedl, Feb,2007):63-67 J: MovieLens Unplugged: Experiences with an [17 Eirinaki, M. and Vazirgiannis, M: Web Mining for Web Occasionally Connected Recommender System. Proc. Int'T Personalization. ACM Trans. On Internet Technology, Vol 3 Conf. intelligent User Interfaces, 2003 (Feb,2003):1-27 [15 Hill, w, Stead, I, Rosenstein, M, and Furnas, G Recommending and Evaluating Choices in a Virtual Community of Use. Proc. Conf. Human Factors i 2118
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 Architecture for User Modeling and Computational Storytelling for Interactive Spaces. In Proceedings of UBicomp 2003, Seattle, WA, USA, Springer. (2003) [14] Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A. and Riedl, J.: MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System. Proc. Int’l Conf. intelligent User Interfaces, 2003 [15] Hill, W., Stead, l., Rosenstein, M., and Furnas, G.: Recommending and Evaluating Choices in a Virtual Community of Use. Proc. Conf. Human Factors in Computing Systems, 1995 [16] Baraglia R., Silvestri F.: Dynamic Personalization of Web Sites Without User Intervention. Comm. ACM, Vol. 50, 2 (Feb., 2007): 63-67. [17] Eirinaki, M. and Vazirgiannis, M.: Web Mining for Web Personalization. ACM Trans. On Internet Technology, Vol. 3, 1 (Feb., 2003): 1-27. 2118
