Social recommender Systems: Recommendations in Support of E-Learning Sheizaf rafaeli: Yuval Dan-Gur: Miri Barak International Journal of Distance Education Technologies, Apr-Jun 2005; 3, 2; ABI/INFORM Global 30 Journal of Distance Education Technologies, 3(2), 30-47, April -June 2005 Social Recommender systems Recommendations in Support of E-Learning Sheizaf Rafaeli, University of Haifa Mt. Carmel, Israel Yuval Dan-Gur, University of Haifa Mt Carmel, Israel Miri Barak, Massachusetts Institute of Technology, USA ABSTRACT Recommendation systems can play an extensive role in online learning In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth In recommender systems, users'ratings can be applied toward items, users, other users ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system-QSIA-an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems, We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation. Keywords: collaborative filtering: friends, knowledge sharing; neighbors; QSIA; recommendations INTRODUCTION communication among learners and trans fer of information. They offer opportuni E-learning involves the use of a com- ties for enhancing ways in which teachers puter or electronic device in some way to teach and learners learn(Hoffman, Wu ng its many Clark and Mayer (2003)define e-learning applications, the Web serves as a tool for as instruction delivered on a computer by way of CD-ROM, Internet, or intranet that Barak, Addir, 2003; Eylon, 2000; Rafaeli is designed to support individual learning or ravid 1997) and the creation of learn organizational performance goals. Th ing communities( Gordin, Gomez, Pea,& Internet and the World Wide Web (www) Fishman, 1997; Sudweeks Rafaeli, 1996) facilitate e-learning by allowing worldwide Ihe spectrum of knowledge items on the Copyright o 2005. Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idca Group Inc, is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Social Recommender Systems: Recommendations in Support of E-Learning Sheizaf Rafaeli; Yuval Dan-Gur; Miri Barak International Journal of Distance Education Technologies; Apr-Jun 2005; 3, 2; ABI/INFORM Global pg. 30
Journal of Distance Education Technologies, 3 (2), 30-47, April-June 2005 31 Internet runs from useful, fascinating, and delivery of popular items, as suggested by important to pointless, bizarre, and mislead Maltz(1994). Konstan et al. (1997)found ing For learners who wish to gain knowl- personalized (rather than impersonal)pre- edge by using information and communi- diction to be significantly more accepted cation technologies(ICT), the actual ben- by users. Recommendations we receive efit of what they stand to gain will be af- daily rely mainly on human-analyzed fected by how well they make discerning sources: movie reviews, rumors, word-of- judgments about what they find( Burbules mouth, surveys, guides, friends, and rec callister, 2000) ommendation literature (Shardanand Judicious use of ICT can boost learn- Maes, 1995; Resnick Varian, 1997) ing that is adapted to the abilities of each Recommender systems approach the prob tudent and enhance the distribution of lem of helping users find preferred items knowledge among users. Psychologists mainly with the technique of Collaborative make distinctions between explicit and tacit Filtering(CF). The basic idea of CF algo- knowledge. Explicit knowledge is the rithms is to predict the likeliness list of the knowledge that can be written down, top-N recommended items based on the whereas tacit knowledge is the knowledge opinions(either explicit or implicit)of like- that lies in the learners'minds Capturing minded users(Sarwar, Karypis, et al and sharing tacit knowledge is extremely 2001); the task is to predict the utility of difficult and was the aim of various studies items for a particular user( the active user), (Kakabadse, Kouzmin, Kakabadse, based on a dataset of users' votes from a 2001). While digitized content in any form sample of population of the other users is explicit knowledge, not many e-learning However, many recommendation systems approaches encourage learners to provide produce unsatisfactory results(Herlocker, their tactic knowledge Konstan, riedl, 2000; Oard Kim, Recommendation systems can play 1998) a large role in online learning as providers Recommendations carry different of tacit knowledge. In such systems, learn- values for the provider, as contrasted with ers can receive guidance in locating and the recommendation seeker. Rafaeli and ranking references, knowledge bits, test Raban(2003)show how the Endowment items, and the like. The core task of a Effect (an extension of the Prospect recommender system is to recommend (in Theory)exists with respect to information a personalized manner)interesting and valu- as well -people value information they able items and to help users make good own much more than information not owned choices from a large number of alterna- by them. Accordingly, research shows that tives without having sufficient personal users tend to ask for recommendations experience or awareness of the alterna- more than to supply them(Avery, Resnick, tives( Gordon, Fan, Rafaeli, Wu, farag, Zeckhauser, 1999; Herlocker, Konstan, 2003: Grasso, Meunier, Thompson, 2000; riedl, 2000). Recommendation systems Oard Kim 1998: Resnick Varian, can be based on human resources--social The task recommend items to a user, algorithms-black-boxes. We argue that hen referenced in recommender systems a large portion of the shortcomings of rec- research, should be interpreted mostly by ommendation systems can be understood personalized manner and not solely by the as a failure to construct social recommen oright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written ssion of Idea Group Inc. is prohibited. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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2 Joumal of Distance Education Technologies, 3(2), 30-47, April-June 2005 dation systems as opposed to black-box items collaboration. Learners should be (non-social)ones. In this chapter, we dis- responsible for recognizing and judging pat- cuss the role of social recommendation terns of information and then organizing systems for supporting learning and com- them, while the computer system should munities of learners In the following sec- perform calculations, store, and retrieve tion, we provide a literature review about information (Jonassen, Carr, Yueh, 1998) the importance of social capital and de- Rather than using the power of computer scribe a recommendation system for knowl- technologies to disseminate information, it edge sharing in learning should be used in all subject domains as tools for engaging learners in reflective, RECOMMENDATION FOR critical thinking about the ideas they are E-LEARNING: studying (Jonassen, Carr, Yueh, 1998) EXPLOITING THE These statements justify another interest- ing e-learning mental model-the one that LEARNING NETWORK recognizes the social and contextual char. AND TACIT KNOWLEDGE acter of e-learning. From this perspective, FOR EFFECTIVE the exploitation of social capital and unexploited tacit knowledge of learners is LEARNING a critical challenge. Open, public views in general and E-learning mental model varies in dif- good recommendations in particular en fere approaches. Several esearchers when facing too anticipate e-learning as a solid technologi- many items, the ability to focus on the best cal phenomenon in which the selection of and ignore the rest is a necessity. The rec a platform can promote the desired learn- ommendation process is a social one ing outcome. In recent years, this approach recommender systems form a community has been evaluated as extremely simplis- of people voting and expressing their opin tic. Several other issues have to be consid- ions about items in a domain on one hand ered in order to expand the value proposi- and seeking recommendations on the other tion of e-learning. Even though the evalua- Being a part of such a community involves tion criteria for efficiency of e-learning sys- social dilemmas-the effort one tends to tems are not agreed upon yet (Lytras et invest in recommending an item(e. g,Am al.,2003a,2003b, 2003c), the dominant sci- I only a small, unimportant part of a large entific opinion is that technology functions group? How much does my specific opin as a mean and not a goal in the learning ion count? As to receiving a recommenda process(Lytras et al., 2003a). In addition, tion, who participates in my recommend learning is influenced by participation in a ing group? On what basis of similarity do community(Bruner, 1990; ygotsky, 1978); opinions aggregate?) learning also involves the use of many re when investigating the role technol sources. In order to sort and select the suit- ogy plays in e-learning systems, Lytras, et able resource, learners seek guidance and al. ( 2003)look at both the process of learnin commendations. The process of seeking and at the product (i.e, learning content in and providing experience-based recommen- terms of learning objects)and reach the dations across users'communities is one conclusion that care should be taken to the way of implementing large-scale knowledge appropriate balance between the role of Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc, is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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Journal of Distance Education Technologies, 3(2), 30-47, April -June 2005 33 technology and pedagogy in e-learning system's quality of performance (some failures of systems are explained by Recommender systems are often accom overestimation of either one as opposed to panied by some misconceptions about their a well customized solution). Modeling the function or process. Many often think that learning process and the learning product recommender systems are exhibits a clear view on the multi-dimen sional spectrum of e-learning systems At no cost, in most cases, the user can (Lytras et al., 2003a); one of its main in eceive free recommendations sights is, when referring to e-learning, A collection of a large number of items knowledge management is not a techno- many of which may not be relevant logical phenomenon; it's a qualitative shift (Grasso, Meunier,& Thompson, 2000 in people's behavior.. "(p. 2580). From Oard Kim, 1998; Resnick& Varian, this perspective, our work further extends 1997) the discussion of the content perceptions Thought to be more objective and ratio in e-learning systems. The diffused knov nal than human advisers(Dijkstra edge within e-learning systems cannot b Liebrand et al., 1998) seen as a solid one, but has to exploit fur-. Thought to be accurate, though evalua- tion of computerized advice is reported In the next section, we go a step fu to be biased(Dijkstra, 1998; Dijkstra ther. We identify a number of challenges or recommendation systems in e-learning,. Thought to be trustworthy, as comput- and we elaborate further their key idea to ward the enhancement of knowledge ex erized systems make information look ploitation in implementations. more credible(Dijkstra, 1998; Dijkstra, 1999: Dijkstra, Liebrand et al., 1998; KEY CHALLENGES OF Murphy Yetmar, 1996) RECOMMENDATION Recommendation systems research SYSTEMS IN E-LEARNING is confronted with this reality: many (if not most) recommendation systems produce a lea due to its huge knowledge repository, unsatisfactory results(Herlocker, Konstan, rning community must develop the Riedl, 2000; Oard Kim, 1998). We ability to mine relevant lea objects have previously listed sor (Lytras et al., 2003c)-the recommenda- nesses related to systems'failures(Rafaeli tion layer in an e-learning system is of great &Dan-Gur,2002): importance to this goal It is important to notice that even ob-.Black boxes: provide no transparency jective indices in the field of recommender into the working of the recommendation systems are not always agreed upon (Herlocker et al., 2000). In the e-learn (Burke, 2002; Pennock, Horvitz Giles, ing context, this fact requires a better fit 2000; Soboroff, Nichols, Pazzani, 1999) of the whole system within the e-learn- and the fact that human taste suffers from ing information systems. It is required noise(Freedman, 1998; Pescovitz, 2000) to integrate recommendations as an in doesn't make it easier to determine a evitable part of the e-learning unique experience Copyright 2005, idea Group Inc, Copying or distributing in print or clectronic forms without written permission of Idea Group Inc is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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34 Journal of Distance Education Technologies, 3(2), 30-47, April-June 2005 Cognitive load effort: a high load is lem in e-learning systems. Learners en- required in the process of assigning ac- roll in courses again and again, and, ac- curate explicit ratings, making it difficult cording to the diffusion model of e-learn to assemble large user populations, thus ing content, they y can pre ontributing to data sparsity(Oard recommendations. The interesting issue Kim, 1998). In e-learning, this issue can for investigation refers to the quality of be addressed by incorporating the rec- the relevant recommendation ommendation process as an integral part Data sparsity and first rater problem: of learning content exploitation. The the number of people who rate items is challenge here is to design transparent relatively small, compared to the num terfaces that capture the recommen- ber of items(Terveen Hill, 2001),es dation without posing extra anxiety to pecially with regard to why anyone e learner while he or she uses the e- should volunteer to rate a new item. In e-learning, the rating of a learning item Exploration/Exploitation tradeoff: incorporated in the learning whether to recommend a wider range process. A functional way is to provide of items about which there is less cer- recommendation, such as a hidden task, tainty or only those which are known to after the completion of the relevant match the user profile learned so far learning session. In this way, the learn- Balabanovic, 1998) ing object paradigm for structuring and Initial user profile: sometimes re- exploiting learning content is quite inter- ferred to as "user model. "this is diffi- esting. A knowledge provider could be cult to form, though it has great impor- the first rater, and consequent tance(Maltz.& Ehrlich, 1995). In an e the learning content can support the learning system, learner profiles can be elaboration managed more effectively. A learner is Performance speed: systems with a a person whose prior knowledge, cogI arge dimensional number (items X us- tive level. and so forth can be outlined ers)slow down online computing per before the learning experience formance(Sarwar, Karypis et al., 2001) New item: a new item in the systems database has no ratings and cant be This list outlines a number of inter- recommended until more data is ob- esting problematic issues that have to be tained(Balabanovic Shoam, 1997). considered in e-learning. The fact is that From this perspective, recommendations e-learning context differentiates from a challenge the learning content creation business environment, since the learning process: The semantic annotation of content has to be valued in terms of learn content can be justified on a social net- ing needs. In other words, recommenda- work basis: Threads of recommenda- tions can be expressed in several different tions from several learners complete the formal metrics with an emphasis on peda value perception of learning content. gogical value, learning need, suitably for Economy of scale: a large user popu- working task, and so forth. In this way,our lation is needed to produce reliable re- work is investigating the social recommend sults( Balabanovic Shoam. 1997: Im ing systems as a key answer to the black Hars, 2001). This fact is not a prob- box approach. This discussion is presented opyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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Journal of Distance Education Technologies, 3(2), 30-47, April- June 2005 35 inthenextsectionandprovidesthebackthisuser(http://find.slatemsn.com/coder ground for our QSIA system theFray/the Fray. asp) as does the Kazaa P2P system. Social comparison theor SOCIAL RECOMMENDING (Festinger, 1954)distinguishes between SYSTEMS: OVERCOMING physical reality and social reality; while THE“ BLACK BOX”FAD the former(physical reality) is usually based on objective scales of reality with no need of others'perspectives, the latter(social Alan Turings test for an intelligent reality)demands examination of and com- machine--requiring that the system seem parison to others. According to the theory intelligent(Turing 1950)-is often men-(Festinger, 1954), people choose similar tioned by HCI researchers(Moon Nass, ones to participate in the comparison pro- 1998: Nass Moon, 2000). Aspiring to cess as only they can satisfy the function pass Turings test may be a necessary but of itable insufficient condition for recommender sys- recommender systems will emphasize the tems Recommender systems goal is not sense of community while black-box sys process automation, but rather process sup- tems. on the other hand can reflect a feel port and augmentation Recommender sys- ing of a user facing an automatic machine tems need to allow for situatedness and whose sole function is to produce recom- peculiarities of human cognition (Lueg& mendations Landolt, 1998), sometimes with the help of User-dependent procedures refer explanations and reasoning(Herlocker et mainly to the influence a user has over the al.,2000) recommendation procedure and We argue that a large portion of the cally, over the formation of the advising shortcomings of recommender systems can group. Hints to the benefits of controlling be understood as a failure to construct so- the recommendation-providers group had cial recommender systems as opposed to been suggested(Herlocker et al., 2000), black-boxnon-social) ones. Table l out- stating that sometimes the user might wish lines some of the proposed distinctions be- to ignore some members of the neighbors tween these two spectrum-edges of sys- group. Existing approaches in tems recommender systems lack the user's con Social awareness can be encour- trol of who advises him or her; the neigh aged by several means. Some of them are bors group is automatically formed and as- group activities and cooperative learning signed by the system according to algo activities(Selman, 2003). Some systems, rithms(Terveen Hill, 2001) especially consumer-focused and e-com- Explicit ratings and explicit user merce ones, such as Amazon. com and models-Recommendation providers and Consumerreview.com,encouragetheseekersexpresstheiropinionsandprefer awareness of the presence of other users ences on items by various means, forms by focusing on the user's rating and re- and scales. These could be dichotomous- marks (Rafaeli Noy, 2002). Epinions. com likes and dislikes(Pazzani, 1999)or, on an fosters a strong sense of awareness of oth- interval-numeric scale(Herlocker et al ers by making their opinion the item under 2000 Maltz Ehrlich 1995: Shardanand concern. The FRAY community, for ex- Maes, 1995), explicit or implicit(Oard ample, enables the user to track more from Kim, 1998), allowing user comments and opyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of ldea Group Inc. is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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36 Journal of Distance Education Technologies, 3(2), 30-47, April- June 2005 Table 1. Social vs. black-box recommender tion patterns(e. g, browsing data, purchase systenLs history, etc. )(Breese, Heckerman et al 1998) The cognitive effort to assign ratings acts as disincentive, making it difficult to assemble large user populations and con- tribute to data sparsity (Oard Kim 1998). Implicit feedback techniques seek 9 to infer ratings that a user would assign f from observations available to the system e A positive correlation has been found be tween reading time in USENet and ex- plicit user ratings( Konstan, Miller et al 分总 1997; Morita shinoda, 1994). Construct a ing a profile with implicit preference rat a ings is neither error proof (Herlocker et al 2000)nor free of privacy-concerns (Ramakrishnan, Keller et al 2001) One should note that explicit rating an environment of social awareness, espe- cially in an anonymous community, migl nomenon al Rosenthal 1964); specific recommendations a large number of other anonymous rec 劇都事制复 Commendations, and the effort, responsibil ity, and thought put into it may be reduced by diffusion Data-driven recommender sys. a s tems are best represented by content- based systems; content-based filtering re- fers to analyzing the information stored in Lim Kim, 2001). Examples are key words-based filtering and latent semantic sa relations between the content of the items and the users stored preferences (Shardanand Maes, 1995). This approach is appropriate when rich content informa- pointers(Maltz& Ehrlich, 1995)or just tion is available in articles and Web pages, votes. Implicit voting refers to interpreting for example( Lim Kim, 2001).One ma user behavior or selections to impute a pref- jor advantage is the possibility for yet un- erence and can be based on any informa- seen items to be recommended. The dis Copyright e 2005. Idea Group Inc. Copying or distributing in print or electronic forms without written Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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Journal of Distance Education Technologies, 3(2), 30-47, April - June 2005 37 advantages are the need for the items to cate that people apply gender stereotypes be in machine-parsed form(e. g, text or and behaviors(e. g, politeness and cogni hypertext), the inability to recommend items tive commitment) based on quality(Shardanand Maes, Moon (1998)suggests, in accordance 1995), and the fact that the system recom- with previous research, that computers are mends only items that scored highly against readily recognized by users as being simi the user's profile-restricting the user to lar or dissimilar to themselves, based on ms similar to those already rated. minimal, text-based manipulations of the No influence of other users and no effect computer's personality. This perceived simi of their experiences are integrated-items larity has major effects on human-computer can be recommended without need for hu- relationships; users are more socially at man evaluation first tracted to similar computers(compared with Balabanovic and Shoam(1997)ob- dissimilar ones) and find the former to be serve that because in pure content-based more intelligent and more enjoyable to in- systems the user's ratings are the only fac- teract with. On one hand, it is difficult to tor influencing future performance, there conceptualize similarity with respect to hu is no way to reduce the quantity of rated man-computer interaction(Moon Nass, items without reducing performance 1998), but, on the other, interfaces that bring a community of shared interests into account the need for co omputer char- a declared goal of some recommender acter are introduced (Ujjin bentley, systems(Kautz, Selman et al., 1997; Linton, 2001) Joy et al., 2000; Resnick Varian, 1997 The tendency to blame computers for Terveen Hill, 2001). Fab system outcomes can also be explained according (Balabanovic Shoam, 1997)identifies to the Attribution Theory of Social Psychol emerging communities of interest in the ogy(Moon Nass, 1998) users' population, enabling enhanced group All of these implications are summa awareness and communications. Maltz and rized in our research in the QSIA system, Ehrlich(1995)describe active collabora- which is presented in the next section tive filtering systems that allow users to send pointers of items to colleagues. QSIA-A SOCIAL Though recommender systems can link a RECOMMENDER SYSTEM ( Terveen Hill, 2001), there needs to be no contact between recommendation pro Qsia(Http: //qsia. org) ducer and receiver(Terveen Hill, 2001), recommender system for Questions Shan ing and Interactive Assignments(Rafael and the procedure is sometimes referred Barak, Dan-Gur Toch, 2004). This online to as computerized oracle(herlocker et al,2000 learning augmentation system was designee Human-computer interaction car to harness the social perspectives discusse previously and to promote collaboration be transformed into the social space: Nass online recommendation, and formation of and Moon(2000) review a series of ex- perimental studies, showing that individu communities in higher education network- als apply social rules and expectations to assisted learning. QSIA is designed to share computers. Their sets of experiments indi- the authoring of test items, their contents and psychometric accumulated history, as Copyright O 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idca Group Inc is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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8 Journal of Distance Education Technologies, 3(2), 30-47, April-June 2005 well as the process of constructing assign- MySQL, a relational database, holds the ments and tests. The system supports the system's data, including the system,s con administration of assignments and tests tent, users'logs, and administrative infor under a variety of contexts: online as well mation. QSIA's structure and functionality as offline, proctored as well as individual, are based on a set of principles, determin- with or without time limits, open or closed ing its construction. The structural concept book, and so forth Straightforward contri- consists of the following principles: open butions to the item database and assign- strands, flexibility, privacy, open source, ease ments templates within any learning domain of use, and multi-community. QSIA widely are only the first tier. Provision of recom- fits the characteristics of a knowledge net mendations for items and assignments is a work as a cooperation of users who share second level of communication. Actual use build, and use a knowledge base(Baets, of the system in a distance-learning capacity 1988) enriches the collected history and available As of September 2003, the system logs. Thus, this system is designed to learn contained more than 1,000 learners and as well as to teach teachers, over 10,000 knowledge items Users(both students and teachers)(sorted to 85 knowledge fields), and thou can access the Qsia environment using sands of recommendations Throughout the anyhttp-compliantWebbrowsersuchasrestofthissectionweshalldemonstrate Microsoft Internet Explorer. Java Applica- how the social characteristics listed earlier tion Server is an application container that in Table I were approached in the design runs the java components, including the and implementation of QSIA.Whenever scriptable Java Server Pages components. relevant, we added a screen snapshot Figure /. OSIA login screen ⊙:回必户smm的m,园口 i Sia beta bel I English/nv I loan I renintrarion Sia system order to access the system for the first time, please folow the 1. Chck on registration, It you are already registered, please Enter the registration code the username password and identification details. Download user guide [ zio) 8288292 send us gmail or ca 972-4 Copyright O 2005, Idea Group Inc. Copying or distributing in print or electronic forms without permission of Idea Group Inc. is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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Journal of Distance Education Technologies, 3(2), 30-47, April-June 2005 39 Figure 2, Recommendation screen in OSIA 毽.ywFt、Iok地 Sia beta Recommendations lends characteristics: 2 Frond 4 Get item o Only teachers o anne 口ynuo E'anu77xnnon As early as the login screen, shown presented in Figure 3. The procedure is as figure 1, QSIA emphasizes the presence relevant in every case where a user of others. (teacher or student) has to make a sele As the user becomes involved in ad- tion(filtering) from the system's database vanced interaction with the system, the (e.g., a teacher selects items for a bundle notations of many other characteristics that or a student is practicing before final exam) accompany other users are revealed To the right of the model, we marked Affiliation of a user -cither a teacher or a student Stage 1: The user faces two options Group membership--according to the browsing and scanning the items to lo- academic institute, faculty, and the spe- cate appropriate ones or using the rec ommendation features. This stage is be Different knowledge areas of users yond the scope of this chapter, although Categories of grades scales choices at this node will be recorded for Figure 2 demonstrates the main Stage 2: Out of all choices to use the screen of the recommendation process recommendation seeking, user can he recommendation process is who advises: neighbors or friends mostly user-controlled: We adopted a five-. Stage 3: The stage is revealed only in stage conceptual model of user interaction cases where a user chooses to consult with the recommendation aspect of QSIA, friends, and it involves the users selec Copyright 2005. Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc is prohibited Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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