2010 International Conference On Computer Design And Appliations(ICCDA 2010 Research Paper Recommendation with Topic analysis ng Pan, Wenxin Li Computer Science Department Peking University fcgp,Iwx@pku.edu.cn Abstrack-with the collaborative filtering techniques becoming there is even no appropriate dataset available for the overall more and more mature, recommender systems are widely used evaluation of research paper recommendation algorithms nowadays, especially in electronic commerce and social A recommender system primarily exploits two kinds of networks. However, the utilization of recommender system in information: users'ratings for items, profiles of users and/or cademic research itself has not received enough attention. a items. Memory-based recommendation makes use of only research paper recommender system would greatly help the users'ratings for items, say the user-item matrix, with endeavor. Due to the textual nature of papers, content each entry the rating of a user for a certain item. There are information could be integrated into existed recommendation ethods. In this paper, we proposed that by using topic model user-based recommendation and item-based recommendation hniques to make topic analysis on research papers, we could [61 User-based recommendation attaches higher weight troduce a thematic similarity measurement into a modified for users who share the similar rating patterns with the active version of item-based recommendation approach. This novel user and calculate the utilities of new items from these ommendation method could considerable alleviate the cold weighted users. Item-based recommendation evaluate an start problem in research paper recommendation. Our items utility for a user by first selecting its most similar experiment result shows that our approach could recommend items that have been rated by the user, then compute the highly relevant research papers. utility as the weighted average of the rating of these similar items. For an online recommender system, when the number ing, research paper of the users grows to a considerable amount, the computation recommendation, latent dirichlet allocation, topic model, cold of similarity of every user pairs rating patterms would be start very time consuming, thi could provide a feasible approach for it computes the . INTRODUCTION similarities between items offline. Reference [8 gives a nic Recommender system appeared in the early 1990s comprehensive survey on recommender systems [12 304, and now they are playing a signi Research paper recommendation would not only uti people's daily lives. Online shopping site Amazon. com and the user-item matrix but also the content information of research papers. We use topic model techniques to make nline movie rental company Netflix recommend topic analysis of research papers and introduced the commodities and services by analyzing customers preferences and online behaviors. Social networking services thematic similarity. By incorporating the thematic similarity ke[14] may suggest that you may know some people or and a modified version of item-based method, we could would possibly like someone to become your friend. There successfully generate highly relevant recommendations and are also interests-based social networking site like [15 provides users with recommendations of books, music CDs, greatly relieve the cold start problem movies, and articles, and recommendations of people who might share the similar tastes based on users ratings for the IL. COLLABORATIVE FILTERING mentioned items In colleges, universities and research institutions, References [567[89]describe the details of the two graduate students, professors, and other researchers need to approaches of memory-based recommendations: user-based find the papers most relevant to their research projects. Thus, recommendation and item-based recommendation, very well. looking for the right papers to read becomes a very important For later convenience we will still elaborate on these twe part of their academic lives. A research paper recommender approaches system will benefit these people in helping the most relevant In a typical recommender system, we have a user set U papers and saving their precious time. Unluckily, paper consists of N users, an item set I consists of M items and a rating set R in which re stands for user us rating for item i research recommender systems have not received enough S, c I stands for the set of items user attention. The currently rare available work includes [16. has rated [17][18], [19]etc. All these work have not taken thematic Collaborative filtering algorithms attempt to compute the information of research papers into recommendation. And expected ratings or say, utilities of items that has not been rated by the active user then present the active user with the 78-1-42447164-5526.0002010IEEE V4-264 Volume 4
20IO International Conference On Computer Design And Appliations (ICCDA 2010) Research Paper Recommendation with Topic Analysis Chenguang Pan, Wenxin Li Computer Science Department Peking University Beijing, China {cgp, lwx}@pku.edu.cn Abstract-With the collaborative filtering techniques becoming more and more mature, recommender systems are widely used nowadays, especially in electronic commerce and social networks. However, the utilization of recommender system in academic research itself has not received enough attention. A research paper recommender system would greatly help researchers to find the most desirable papers in their fields of endeavor. Due to the textual nature of papers, content information could be integrated into existed recommendation methods. In this paper, we proposed that by using topic model techniques to make topic analysis on research papers, we could introduce a thematic similarity measurement into a modified version of item-based recommendation approach. This novel recommendation method could considerable alleviate the cold start problem in research paper recommendation. Our experiment result shows that our approach could recommend highly relevant research papers. Keywords: collaborative jiltering, research paper recommendation, latent dirichlet allocation, topic model, cold start I. INTRODUCTION Recommender system appeared in the early 1990s [1][2][3][4], and now they are playing a significant role in people's daily lives. Online shopping site Amazon.com and online movie rental company Netflix recommend commodities and services by analyzing customers' preferences and online behaviors. Social networking services like [14] may suggest that you may know some people or would possibly like someone to become your friend. There are also interests-based social networking site like [15] provides users with recommendations of books, music CDs, movies, and articles, and recommendations of people who might share the similar tastes based on users' ratings for the mentioned items. In colleges, universities and research institutions, graduate students, professors, and other researchers need to find the papers most relevant to their research projects. Thus, looking for the right papers to read becomes a very important part of their academic lives. A research paper recommender system will benefit these people in helping the most relevant papers and saving their precious time. Unluckily, paper research recommender systems have not received enough attention. The currently rare available work includes [16], [17], [18], [19] etc. All these work have not taken thematic information of research papers into recommendation. And there is even no appropriate dataset available for the overall evaluation of research paper recommendation algorithms. A recommender system primarily exploits two kinds of information: users' ratings for items, profiles of users and/or items. Memory-based recommendation makes use of only the users' ratings for items, say, the user-item matrix, with each entry the rating of a user for a certain item. There are basically two kinds of memory-based recommendations: user-based recommendation and item-based recommendation [6] [7]. User-based recommendation attaches higher weights for users who share the similar rating patterns with the active user and calculate the utilities of new items from these weighted users. Item-based recommendation evaluate an item's utility for a user by first selecting its most similar items that have been rated by the user, then compute the utility as the weighted average of the rating of these similar items. For an online recommender system, when the number of the users grows to a considerable amount, the computation of similarity of every user pair's rating patterns would be very time consuming, thus item-based recommendation could provide a feasible approach for it computes the similarities between items offline. Reference [8] gives a nice comprehensive survey on recommender systems. Research paper recommendation would not only utilize the user-item matrix but also the content information of the research papers. We use topic model techniques to make topic analysis of research papers and introduced the similarity over topics between research papers as the thematic similarity. By incorporating the thematic similarity and a modified version of item-based method, we could successfully generate highly relevant recommendations and greatly relieve the cold start problem. II. COLLABORATIVE FILTERING References [5][6][7][8][9] describe the details of the two approaches of memory-based recommendations: user-based recommendation and item-based recommendation, very well. For later convenience we will still elaborate on these two approaches. In a typical recommender system, we have a user set U consists of N users, an item set I consists of M items and a rating set R in which rlli stands for user u's rating for item i. SII � I stands for the set of items user u has rated. Collaborative filtering algorithms attempt to compute the expected ratings or say, utilities of items that has not been rated by the active user, then present the active user with the 978-1-4244-7164-5/$26.00 © 2010 IEEE V4-264 Volume 4
2010 International Conference On Computer Design And Appliations(ICCDA 2010 items with the highest utilities. We will describe below the Pearson correlatio details of relevant memory-based recommendations: user- ased recommendation item-based recommendation and our modified version of item-based recommendation ssy。-rkr-r) A. User-based Recommendation In a collaborative filtering algorithm we are to the utility Pat of the active user a on a given item i SS ra)zies Sruiru not been rated by this user. Since the item i is not user a. thus we have ig s. For user-based recommendation Simple cosine: the utility of an unrated item for a user is based on the ratings lat item of t measure between users need to be defined and a set of cosine(a, u)= ∑ssn×r nearest neighbors to be selected. Then we combine the ratings of these neighbors on the item in some way sas rameses ira The way we can define the similarity between users will be described in A.1). Let sim(a, u be similarity betweer There are also other similarity definitions as Manhattan users a and u. The number of neighbors to be considered is similarity, Jaccard similarity, Tanimoto score but we will often set by a system parameter that we denote by k, k is not describe them here often set to N, the total number of users in the system. So the set of neighbors of a give user a, denoted by To, is made of B. Item-based Recommendation e their similarity to user a. In the item-based approach, Pai, the utility of an unrated already rated item i item i. We denote the set of these K items as Ik. rk stands for rating of item ik Elk. So the utility of the unrated item i is ∑rssm(nrm xSim(i, ik) ∑rsma ∑sim(i) Like the user-based approach, we also have a method ating scale by different users, predictions b user of compute utility of i using deviation from the mean rating In order to take into account the difference in sed on given by user a. deviations from the mean ratings have been proposed In that If we use the recommendation provided by formula(6) case,Pa is computed using the sum of the user mean rating item or has given a few items the same rating, utilities of all the neighbors that have rated item i case no unrated item could be given preference in ∑ tvs. jsim(a, u)(rn-r) recommendation (2 Since we hope our recommendation could give reliable Im(a u recommendations even when the user has just rated very few items or even one item. We will use a modified version of em-based recommend r. represents the mean rating of user u: Pr=2r,xsim(i, ik) K This formula simply weights the ratings of items (3 according to their respective similarity with the current item being evaluated, and computes an average Ⅲ. TOPIC ANALYSIS Topic models [10J[11]are based upon the idea that with The similarity defined between users or items include but in text corpora, a document is a mixture of topics, where a are not limited to the following ways topic is a multinomial distribution over words. The latent dirichlet allocation(LDA)model (or"topic model")is a probabilistic procedure by which documents can be generated. The mixing coefficients for each document and
2010 International Conference On Computer Design And Appliations (ICCDA 2010) items with the highest utilities. We will describe below the details of relevant memory-based recommendations: userbased recommendation, item-based recommendation and our modified version of item-based recommendation. A. User-based Recommendation In a collaborative filtering algorithm we are to compute the utility Pai of the active user a on a given item i that has not been rated by this user. Since the item i is not rated by user a, thus we have irl. Sa. For user-based recommendation, the utility of an unrated item for a user is based on the ratings of that item of the nearest neighbors of the user. A similarity measure between users need to be defined and a set of nearest neighbors to be selected. Then we combine the ratings of these neighbors on the item in some way. The way we can define the similarity between users will be described in A.I). Let sim( a, u) be similarity between users a and u. The number of neighbors to be considered is often set by a system parameter that we denote by K, K is often set to N, the total number of users in the system. So the set of neighbors of a give user a, denoted by Ta, is made of the K users that maximize their similarity to user a. We define the utility of item i for user a as the weighted sum of the ratings of the nearest neighbors uE Ta that have already rated item i: (1) In order to take into account the difference in user of rating scale by different users, predictions based on deviations from the mean ratings have been proposed. In that case, Pai is computed using the sum of the user mean rating and the weighted sum of deviations from their mean rating of the neighbors that have rated item i: - L{uqYEsJsim(a,u). (rUi - rJ Pai� r + (2) a L�,qYESjlsim(a,u)1 r represents the mean rating of user u: u (3) 1) Similarity Measurements The similarity defined between users or items include but are not limited to the following ways: Pearson correlation: pearson(a, u) = (4) Simple cosine: (5) There are also other similarity definitions as Manhattan similarity, Jaccard similarity, Tanimoto score, but we will not describe them here. B. Item-based Recommendation In the item-based approach, Pai, the utility of an unrated item i for the user a is computed as follows: We choose the most similar rated K items by user a for item i. We denote the set of these K items as h. rk stands for rating of item h E h. So the utility of the unrated item i is: L r k X sim(i, ik) Pai = L sim(i, ik) (6) Like the user-based approach, we also have a method to compute utility of i using deviation from the mean rating given by user a. If we use the recommendation provided by formula (6), we could easily find out that when the user has just rated one item or has given a few items the same rating, utilities of all the unrated items will be the same as the given rating. In that case no unrated item could be given preference in recommendation. Since we hope our recommendation could give reliable recommendations even when the user has just rated very few items or even one item. We will use a modified version of item-based recommendation: Pai= Lrkxsim(i, ik) K (7) This formula simply weights the ratings of items according to their respective similarity with the current item being evaluated, and computes an average. III. TOPIC ANALYSIS Topic models [10] [11] are based upon the idea that with in text corpora, a document is a mixture of topics, where a topic is a multinomial distribution over words. The latent dirichlet allocation (LDA) model (or "topic model") is a generative model for documents: it specifies a simple probabilistic procedure by which documents can be generated. The mixing coefficients for each document and V4-265 Volume 4
2010 International Conference On Computer Design And Appliations(ICCDA 2010 the word-topic distributions are unobserved and are learned p k, w] the multinomial coefficient for word w in to from data using unsupervised learning methods k LDA can be described as finding a mixture of topics for each document, i. e, P(z d), with each topic described by j∈[,D,w∈[,W,k∈[l,K words following another probability distribution, i. e, P(t z) We define the thematic similarity between two research where P(tI d)is the probability of the ith word for a given Thematic._Sim(p, g)= e(p/e nd a, Ov This can be formalized as papers p and g as the cosine similarity defined over their PG|d=∑P(lz=P(=jd (8) topic mixture coefficient vector (12) document d and z, is the latent topic. P(t, I zi=n is the probability of t, within topic P(z/=j d is the probability of generating a word from topic j in the document d. The As we know, there are a variety of ways to define the number of latent topics Z is defined in advance and controls similarity between vectors, but in our experiment we simply the granularity of differences among latent topics. LDA choose simple cosine similarity for the computation of estimates the topic-word distribution P(t z document-topic distribution P(z d)from an In our recommendation method we use thematic corpus of documents using Dirichlet priors similari ity to replace the original similarity get from user-item distributions and a fixed number of topics matrix. combined with modified item-based method we Reference Blei et al [11] introduced the LDA model could make research paper recommendations for users. This within a general Bayesian framework and developed a recommendation method could considerably alleviate cold variational algorithm for learning the model from data. start problem of recommender systems. In a typical Reference Griffiths and Steyvers [12] subsequently proposed recommender system, cold start problem has two situations a learning algorithm based on Gibbs sampling. The Gibbs One is that when a user has just ranked very few items, it is mpling approach iterates multiple times over each word t, very difficult to generate recommendations accurately due to in document d, and samples a new topic j for the word based the little information of the user we have. The other situation on Equation(8), until the LDA model parameters converge. is that when a new item has just been registered in the system, no user has yet got time to rate the item, so that this item will B not get the slightest chance to be recommended. For most recommender system the first situation is more severe and P(z= jl ta d,2z-)∝ ∑Cn+7BΣCa2+2 (9) more common than the second situation. Our method could relieve both the two situations greatly, especially the first C maintains a count of all topic-word assignments, C counts the document-topic assignments, z. represents all IV. EXPERIMENT topic-word and document-topic assignments except the current assignment z, for word ts a and B are the we will demonstrate the result all along, and we will make hyperparameters for the dirichlet priors, serving as data and result available online for the convenience of other smoothing parameters for the counts. Based on the counts researchers the posterior probabilities in Equation(8)can be estimated as e take 122 research papers from the research paper collection of our laboratory, mainly on biometrics research, as the research papers that are going to be used in our P(4|z;=) (10) recommender system. They are all in Portable Document ∑Cn+7B Format(PDF) Then all these pdf files are converted into text using a Java library called Apache PDF Box [22). These files an be download at 201. We preprocessed these text files and use a perl script David Newman to make these research papers into UCI Bag Of-Words Dataset format [21]. The dataset generated by us Simply put, we are going to use Gibbs sampling methods is available here [20]. e the mixture coefficients f each topic in We implemented the Gibbs sampling algorithm to document,and the coefficients of a topic's multinomial process the dataset we generated. We took 40 topics, and a=2.0/40, B=0.01 as the parameters in our inference.We words appeared within, N words in total, and we suppose successfully, computed the coefficient of topic mixture in there are K topics in the corpora. Using Gibbs sampling for each research paper and the multinomial coefficient for each LDa we could obtain the results as follows: BU, k], the mixture coefficient for topic k in document j v4-266 Volume 4
2010 International Conference On Computer Design And Appliations (ICCDA 2010) the word-topic distributions are unobserved and are learned from data using unsupervised learning methods. LDA can be described as finding a mixture of topics for each document, i.e., P(z I d), with each topic described by words following another probability distribution, i.e., pet I z). This can be formalized as z P(1i I d) = L P(ti I Zi = j)P(Zi = j I d) (8) j='l where P(ti I d) is the probability of the ith word for a given document d and Zi is the latent topic. P(ti I Zi = j) is the probability of ti within topic j. P(Zi = j I d) is the probability of generating a word from topic j in the document d. The number of latent topics Z is defined in advance and controls the granularity of differences among latent topics. LDA estimates the topic-word distribution pet I z) and the document-topic distribution P(z I d) from an unlabeled corpus of documents using Dirichlet priors for the distributions and a fixed number of topics. Reference Blei et al [11] introduced the LDA model within a general Bayesian framework and developed a variational algorithm for learning the model from data. Reference Griffiths and Steyvers [12] subsequently proposed a learning algorithm based on Gibbs sampling. The Gibbs sampling approach iterates multiple times over each word ti in document di, and samples a new topic j for the word based on Equation (8), until the LDA model parameters converge. C TZ+ fJ CDZ +a t,J d;l P(Zi = j I ti, di, Z_i) ex: (9) " C1Z +TfJ" C DZ L.. +Za t If L..z d,z CTZ maintains a count of all topic-word assignments, (!Yz counts the document-topic assignments, Z_i represents all topic-word and document-topic assignments except the current assignment Zi for word ti, a and fJ are the hyperparameters for the Dirichlet priors, serving as smoothing parameters for the counts. Based on the counts the posterior probabilities in Equation (8) can be estimated as follows: DZ . Cd,; +a P(Zi = ] I di) = ---':-::---- z z +Za (10) (11) Simply put, we are going to use Gibbs sampling methods to compute the mixture coefficients of each topic in a document, and the coefficients of a topic's multinomial distribution over words. Say we have D documents in the corpora, W unique words appeared within, N words in total, and we suppose there are K topics in the corpora. Using Gibbs sampling for LDA we could obtain the results as follows: 8 U, k], the mixture coefficient for topic k in documentj. rp [k, w], the multinomial coefficient for word w in topic k. jE[I, D], WE[I, W), kE[I, K]. We define the thematic similarity between two research papers p and q as the cosine similarity defined over their topic mixture coefficient vector 8[p) and 8[q] : .. 8[p] . 8[ q ] Thematic � Slm(p, q ) = 118[p ]1111fJ[ q ] 11 (12) As we know, there are a variety of ways to define the similarity between vectors, but in our experiment we simply choose simple cosine similarity for the computation of recommendations. In our recommendation method we use thematic similarity to replace the original similarity get from user-item matrix, combined with modified item-based method, we could make research paper recommendations for users. This recommendation method could considerably alleviate cold start problem of recommender systems. In a typical recomender system, cold start problem has two situations. One is that when a user has just ranked very few items, it is very difficult to generate recommendations accurately due to the little information of the user we have. The other situation is that when a new item has just been registered in the system, no user has yet got time to rate the item, so that this item will not get the slightest chance to be recommended. For most recommender system the first situation is more severe and more common than the second situation. Our method could relieve both the two situations greatly, especially the first situation. IV. EXPERIMENT Our experiment is carried in the following procedure and we will demonstrate the result all along, and we will make data and result available online for the convenience of other researchers. We take 122 research papers from the research paper collection of our laboratory, mainly on biometrics research, as the research papers that are going to be used in our recomender system. They are all in Portable Document Format (PDF). Then all these PDF files are converted into text files using a Java library called Apache PDF Box [22]. These text can be download at [20]. We preprocessed these text files and use a perl script by David Newman to make these research papers into uel BagOf-Words Dataset format [21]. The dataset generated by us is available here [20]. We implemented the Gibbs sampling algorithm to process the dataset we generated. We took 40 topics, and a =2.0/40, fJ =0.01 as the parameters in our inference. We successfully computed the coefficient of topic mixture in each research paper and the multinomial coefficient for each V4-266 Volume 4
2010 International Conference On Computer Design And Appliations(ICCDA 2010 TABLEL TOPICS MIXTUREIN DOC 59 TABLE IIL WORDS GENERATION PROBABILITY IN TOPIC 32 TOPIC ID TOPIC WEIGHT WORD ID PROBABILITY 00324735 00166862 calibration word to be generated in each topic. Results are available at [20]. Table 1 shows the coefficients of topic mixture in Doc. 59:"A Novel Scheme for Fingerprint Identification"by Tsong- Liang Huang et al. Only 12 topics with highest mixture weight are show in Table 1. It is very obvious that 000977183 this research paper is mainly about Topic 32 and Topic 27 architecture Topics 32 and 27s probability to generate each word is 000856932 shown in Table 3 and 4. Only 20 words with the h are s Then we computed the thematic similarities between all TABLE IV. WORDS GENERATION PROBABILITY IN TOPIC 27 possible pairs of research papers. Results are available here 20]. We can easily find that the thematic similarity between WORD ID PROBABILITY Doc. 59 and Doc. 61 is 0.99821355. The topic weights of processing Doc, 61 are shown in Table. 2. Doc, 61 is "A Pixel-Level Automatic Calibration Circuit Scheme for Capacitive Fingerprint Sensor LSIs"by hiroki Morimura et al. We can research asily find out that these two papers are quite similar in their main topIcs In the recommendation scenario we consider a user that engineering has only rated 3 papers: Doc. 14, 31, 59, with respective rating 1,-2, 2. We take rating -2,-1, 0, 1, 2 to stand for We successfully use our recoll Ion Igorithm generate the recommendations with the highest predicted Utility for this user. The recommendation result is available here[20] and partly shown in Table 5 on next page design Relevant Doc. 14, 31, 58, 55, 49, 48 are"Estimation and sample size calculations for correlated binary error rates of biometric identification devices"by Michael E. Schuckers "validating a Biometric Authentication System: Sample Size Requirements”by Sarat C. Dass et al.,"A Multichannel Approach to Fingerprint Classification" by Anil K. Jain et al.,"A TABLE IL TOPICS MIXTURE IN DOC. 61 Fingerprint Verification System Based on Triangular TOPICID TOPIC WEIGHT Matching and Dynamic Time Warping"by Zsolt Mikos Kovacs-Vajna," A 600-dpi Capacitive Fingerprint Sensor 0132365 Chip and Image-Synthesis Technique "by Jeong-Woo Lee et al.. "Theoretical statistical correlation for biometric identification performance"respectively The recommendation list in table 5 shows us that undet he influence of Doc. 59, which got the highest rating 2, the Doc. 61 which is highly thematically similar to Doc. 59 as demonstrated above rank the first in the recommendation list. The utility of Doc. 61 is not as high as close to Doc 59s 00578796 rating is due to the influence of the worst rated item: Doc. 31 Volume 4
2010 International Conference On Computer Design And Appliations (ICCDA 2010) TABLE!. TOPICS MIXTURE IN Doc. 59 TOPIC ID TOPIC WEIGHT 32 0.70430677 27 0.12807462 26 0.06082450 5 0.04884846 38 0.02535698 8 0.01476278 35 0.00831414 23 0.00370797 6 0.00324735 10 0.00140488 33 0.00048365 11 0.00002303 word to be generated III each tOpIC. Results are aVaIlable at [20). Table l. shows the coefficients of topic mixture in Doc. 59: "A Novel Scheme for Fingerprint Identification" by Tsong-Liang Huang et aI. Only 12 topics with highest mixture weight are show in Table I. It is very obvious that this research paper is mainly about Topic 32 and Topic 27. Topics 32 and 27's probability to generate each word is shown in Table 3 and 4. Only 20 words with the highest probability are shown. Then we computed the thematic similarities between all possible pairs of research papers. Results are available here [20). We can easily find that the thematic similarity between Doc. 59 and Doc.61 is 0.99821355. The topic weights of Doc. 61 are shown in Table. 2. Doc. 61 is "A Pixel-Level Automatic Calibration Circuit Scheme for Capacitive Fingerprint Sensor LSIs" by Hiroki Morimura et aI. We can easily find out that these two papers are quite similar in their main topics. In the recommendation scenario we consider a user that has only rated 3 papers: Doc. 14, 31, 59, with respective rating 1, -2, 2. We take rating -2, -1, 0, 1, 2 to stand for "awful", "bad", "so-so", "good", "awesome". We successfully use our recommendation algorithm to generate the recommendations with the highest predicted utility for this user. The recommendation result is available here [20] and partly shown in Table 5 on next page. Relevant Doc. 14, 31, 58, 55, 49, 48 are "Estimation and sample size calculations for correlated binary error rates of biometric identification devices" by Michael E. Schuckers, "Validating a Biometric Authentication System: Sample Size Requirements" by TABLE I!. TOPICS MIXTURE IN Doc. 61 TOPIC ID TOPIC WEIGHT 32 0.65057699 27 0 13236567 26 0.06168410 38 0.03968626 5 0.03247386 24 0.02490083 35 0.01660656 21 0.01011540 28 0.00903354 8 0.00867292 16 0.00578796 23 0.00398485 TABLE Ill. WORDS GENERATION PROBABILITY IN TOPIC 32 WORDID PROBABILITY WORD 3240 0.08928722 sensor 745 0.05155874 circuit 731 0.04073623 chip 1565 0.03562560 fingerprint 2704 0.03487404 pixel 3237 0.02705778 sensing 1870 0.02014340 image 2056 0.01773840 japan 1545 0.01668621 fig 633 0.01398058 calibration 3301 0.01232714 signal 1562 0.01067370 finger 3541 0.01067370 surface 2712 0.01007245 plate 404 0.00992214 area 781 0.00992214 cmos 1323 0.00977183 element 412 0.00886995 array 401 0.00871964 architecture 1551 0.00856932 film TABLE IV. WORDS GENERA nON PROBABILITY IN TOPIC 27 WORDID PROBABILITY WORD 1857 0.05555788 ieee 2844 0.04356642 processing 3622 0.02695439 test 3301 0.02618429 signal 3085 0.02508416 research 2969 0.02255385 received 2841 0.02211380 process 1355 0.02035358 engineering 3597 0.01760325 technology 3795 0.01617307 university 2311 0.01320271 member 1317 0.01155251 electrical 1948 0.01133248 information 1047 0.01100244 data 1094 0.01089243 degree 1135 0.01056239 design 1189 0.00946225 digital 1992 0.00946225 interest 1111 0.00935224 department 872 0.00891219 computer Sarat C. Dass et aI., "A Multichannel Approach to Fingerprint Classification" by Anil K. Jain et aI., "A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping" by Zsolt Mikos Kovacs-Vajna, "A 600-dpi Capacitive Fingerprint Sensor Chip and Image-Synthesis Technique" by Jeong-Woo Lee et aI., "Theoretical statistical correlation for biometric identification performance" respectively. The recommendation list in Table 5 shows us that under the influence of Doc. 59, which got the highest rating 2, the Doc.61 which is highly thematically similar to Doc. 59 as demonstrated above, rank the first in the recommendation list. The utility of Doc. 61 is not as high as close to Doc. 59's rating is due to the influence of the worst rated item: Doc. 31. V4-267 Volume 4
2010 International Conference On Computer Design And Appliations(ICCDA 2010) TABLE V TOP5 RECOMMENDATIONS collaborative filtering to DOCID PREDICTED UTILITY Usenet news", Comm. ACM, Vol40, no.3, 77-87, 1997 []J.S. Breese, D. Heckerman and C. Kadie, "Empirical analysis of Uncertainty in Artificial Intellligence, July 1998 [6]BSarwar, G. Karypis, JKonstan and J. Riedl,"Item-Based collaborative filterin 10"In www Conf. 2001 The thematic similarity between Doc. 61 and lowest rated [7]g. Linden, B. Smith and J. York,"amazon.coM recommendations Doc. 31 reduced the utility of Doc. 61 to below I but still Jan/Feb. 200 remains the highest. We can also see that utility of research [8)G ius and A. Tuzhilin, "Toward the next generation of papers with highest thematic similarity to Doc. 31 have very der systems: a survey of the state-of-the-art and possible low or even negative utilities. For example, Doc. 21 name Extensi EEE Trans. on Knowledge and Data Engineering 17, "Effects of User Correlation on Sample Size Requirements' (2005),634-749 by Sarat C. Dass and Anil K. Jain, is predicted a utility of- 9 L. Candillier, F. Meyer and F Fessant, Designing specific weighted 0.55714472. because this er has a high thematic similarity at 0.99470842 with Doc. 31 [10 T. Hoffman, "Probabilistic latent semantic analysis", UAl99 V. CONclusiON [11] D Blei, A. Ng and M. Jordan, "Latent dirichlet allocation", Joumal of Machine Learning Research, 3: 993-1022, Jan. 2003 Recommender systems are a powerful technology for [12] T Griffiths and M Steyvers, "Finding scientific topics"PNAS/USA electronic commerce and social networks. They can help a 101 Supp l1:5228-5235,Apr.2004 business to increase sales revenue and help customers to find [13] 1. Porteous et al. "Fast collapsed Gibbs sampling for latent dirichlet product to their liking, or help people to find friends whe allocation", KDDO8 Aug 2008 harethesametasteforthingsAtthesametimetheir[14]www.facebook.com potentialimpactonscientificresearchislargelyunexplored[15]www.douban.com We proposed a novel method for research paper [16]CBasu, H Hirsh, w.W. C. Nevill-Manning, "Technical recommendation. We proved by our experiment that by ing multiple information making topic analysis on research papers and introducing sources" Journal of machir g Research1(2001)231-252 thematic similarity we could recommend highly relevant [ T.Y. Tang and G A multidimensional paper papers and considerably alleviate the cold start problem experiments and evaluations", 2009 IEEE Internet Even when the user has just rated very few papers, we could still generate satisfactory recommendations [18 M. Gori and A. Pucci, Research paper recommender systems: a Conf on Web Intelligence. ACKNOWLEDGMENT [19]T Boge We would like to thank the developers of Apache using CiteULike", ACM Recsys08 PdfbOx,andDaveNewmanforhisnicelyworkingperl[20]https://docs.google c?id=OBw3EfwDJYdmXZDg 3NDViYzctN script. Thank you for your work, so that we dont need to do E3MiOOODJkLW12YTMtZJYxNDY3NDNkOT( ad&h=en everything from scratch. 221]http://archive.icsuci.edu/ml/datasets/bagtoftwords REfERENCES hols, B. M. Oki and D. [2] P Resnick et al filtering of netnews", Proc. ACM 1994 Conf. Computer Supported Cooperative Work, ACM Press, 1994, 175-186 3] UShardanand, P. Maes, "Social information filtering: algorithms for automating*word of mouth", ACM 1995 Conf. Human Factors in Computing Systems, Voll, 21 v4-268 Volume 4
2010 International Conference On Computer Design And Appliations (ICCDA 2010) TABLE V. TOP 5 RECOMMENDATIONS DOC ID PREDICTED UTILITY 61 0.66960195 58 0.66002356 55 0.49133007 49 0.37691359 48 0.37416252 The thematic similarity between Doc. 61 and lowest rated Doc. 3 I reduced the utility of Doc. 61 to below 1 but still remains the highest. We can also see that utility of research papers with highest thematic similarity to Doc. 31 have very low or even negative utilities. For example, Doc. 21 named "Effects of User Correlation on Sample Size Requirements" by Sarat C. Dass and Anil K. Jain, is predicted a utility of - 0.55714472, because this paper has a high thematic similarity at 0.99470842 with Doc. 31. V. CONCLUSION Recommender systems are a powerful technology for electronic commerce and social networks. They can help a business to increase sales revenue and help customers to find product to their liking, or help people to find friends who share the same taste for things. At the same time their potential impact on scientific research is largely unexplored. We proposed a novel method for research paper recommendation. We proved by our experiment that by making topic analysis on research papers and introducing thematic similarity we could recommend highly relevant papers and considerably alleviate the cold start problem. Even when the user has just rated very few papers, we could still generate satisfactory recommendations. ACKNOWLEDGMENT We would like to thank the developers of Apache PDFBox, and Dave Newman for his nicely working perl script. Thank you for your work, so that we don't need to do everything from scratch. REFERENCES [I) D. Goldberg, D. Nichols, B. M. Oki and D. Terry, "Using collaborative filtering to weave an information tapestry", Communication of the ACM 35 (1992), 61-70 [2) P. Resnick et a!., "GroupLens: an open architecture for collaborative filtering of netnews", Proc. ACM 1994 Conf Computer Supported Cooperative Work, ACM Press, 1994, 175-186 [3) UShardanand, P. Maes, "Social information filtering: algorithms for automating 'word of mouth''', ACM 1995 Conf Human Factors in Computing Systems, Vol I , 210-217 [4) lA. Konstan et a!. "Group Lens: applying collaborative filtering to Usenet news", Comm. ACM, VoI40,no.3, 77-87,1997 [5) lS. Breese, D. Heckerman and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering", Proc. 14th Conf Uncertainty in Artificial Intellligence, July 1998 [6) B.Sarwar, G. Karypis, lKonstan and I Riedl, "Item-Based collaborative filtering recommendation algorithms", Proc. 10th In!' I WWW Conf. 2001 [7) G. Linden, B. Smith and 1 York, "Amazon. com recommendations: item-to-item collaborative filtering", IEEE Internet Computing, Jan.lFeb. 2003 [8) G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible Extensions", IEEE Trans. on Knowledge and Data Engineering 17, (2005), 634-749 [9) L. Candillier, F. Meyer and F. Fessant, "Designing specific weighted similarity measures to improve collaborative filtering systems", ICDM 2008, LNAI 5077, 242-255 [10) T. Hoffman, "Probabilistic latent semantic analysis", UAI'99 [II) D. Blei, A. Ng and M. Jordan, "Latent dirichlet allocation", Journal of Machine Learning Research, 3:993-1022, Jan. 2003 [12) T. Griffiths and M. Steyvers, "Finding scientific topics", PNASI USA, 101 Supp 115228-5235, Apr. 2004 [13) I. Porteous et a!. "Fast collapsed Gibbs sampling for latent dirichlet allocation", KDD'08 Aug. 2008 [14) www.facebook.com [15) www.douban.com [16) C. Basu, H. Hirsh, W.W Cohen and C. Nevill-Manning, 'Technical paper recommendation: a study in combining multiple information sources", Journal of Machine Learning Research 1 (2001) 231-252 [17) T Y Tang and G. McCalla, "A multidimensional paper recommender - experiments and evaluations", 2009 IEEE Internet Computing [18) M. Gori and A. Pucci, "Research paper recommender systems: a random-walk based approach", Proc. of 2006 IEEEIWICIACM Int'l Conf on Web Intelligence. [19) T. Bogers and A. van den Bosch, "Recommending scientific articles using CiteULike", ACM Recsys'08 [20) https://docs.google.com/uc?id=OBw3EfWDJYdmXZDg3NDViYzctN 2E3MiOOODJkL WI2YTMtZjYxNDY3NDNkOTQx&export=downlo ad&hl=en [21) http://archive.ics.uci.edulmlldatasets/Bag+of+Words [22) http://pdfbox.apache.org/ V4-268 Volume 4
