research-article

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

Authors:

Gediminas Adomavicius,

Alexander TuzhilinAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 17, Issue 6

Pages 734 - 749

https://doi.org/10.1109/TKDE.2005.99

Published: 01 June 2005 Publication History

Abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

References

[1]

G. Adomavicius and A. Tuzhilin, “Expert-Driven Validation of Rule-Based User Models in Personalization Applications,” Data Mining and Knowledge Discovery, vol. 5, nos. 1 and 2, pp. 33-58, 2001a.

Digital Library

[2]

G. Adomavicius and A. Tuzhilin, “Multidimensional Recommender Systems: A Data Warehousing Approach,” Proc. Second Int'l Workshop Electronic Commerce (WELCOM '01), 2001b.

Digital Library

[3]

G. Adomavicius R. Sankaranarayanan S. Sen and A. Tuzhilin, “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach,” ACM Trans. Information Systems, vol. 23, no. 1, Jan. 2005.

Digital Library

[4]

C.C. Aggarwal J.L. Wolf K-L. Wu and P.S. Yu, “Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering,” Proc. Fifth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, Aug. 1999.

Digital Library

[5]

A. Ansari S. Essegaier and R. Kohli, “Internet Recommendations Systems,” J. Marketing Research, pp. 363-375, Aug. 2000.

[6]

J.S. Armstrong, Principles of Forecasting-A Handbook for Researchers and Practitioners. Kluwer Academic, 2001.

[7]

R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley, 1999.

Digital Library

[8]

M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,” Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.

Digital Library

[9]

C. Basu H. Hirsh and W. Cohen, “Recommendation as Classification: Using Social and Content-Based Information in Recommendation,” Recommender Systems. Papers from 1998 Workshop, Technical Report WS-98-08, AAAI Press 1998.

[10]

N. Belkin and B. Croft, “Information Filtering and Information Retrieval,” Comm. ACM, vol. 35, no. 12, pp. 29-37, 1992.

Digital Library

[11]

D. Billsus and M. Pazzani, “Learning Collaborative Information Filters,” Proc. Int'l Conf. Machine Learning, 1998.

Digital Library

[12]

D. Billsus and M. Pazzani, “A Personal News Agent that Talks, Learns and Explains,” Proc. Third Ann. Conf. Autonomous Agents, 1999.

Digital Library

[13]

D. Billsus and M. Pazzani, “User Modeling for Adaptive News Access,” User Modeling and User-Adapted Interaction, vol. 10, nos. 2-3, pp. 147-180, 2000.

Digital Library

[14]

D. Billsus C.A. Brunk C. Evans B. Gladish and M. Pazzani, “Adaptive Interfaces for Ubiquitous Web Access,” Comm. ACM, vol. 45, no. 5, pp. 34-38, 2002.

Digital Library

[15]

J.S. Breese D. Heckerman and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence, July 1998.

Digital Library

[16]

M.D. Buhmann, “Approximation and Interpolation with Radial Functions,” Multivariate Approximation and Applications, N. Dyn, D.nbspLeviatan, D. Levin, and A. Pinkus, eds., Cambridge Univ. Press, 2001.

[17]

R. Burke, “Knowledge-Based Recommender Systems,” Encyclopedia of Library and Information Systems, A. Kent, ed., vol. 69, Supplement 32, Marcel Dekker, 2000.

[18]

A. Caglayan M. Snorrason J. Jacoby J. Mazzu R. Jones and K. Kumar, “Learn Sesame-A Learning Agent Engine,” Applied Artificial Intelligence, vol. 11, pp. 393-412, 1997.

[19]

S. Chaudury and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” ACM SIGMOD Record, vol. 26, no. 1, pp.nbsp65-74, 1997.

Digital Library

[20]

Y.-H. Chien and E.I. George, “A Bayesian Model for Collaborative Filtering,” Proc. Seventh Int'l Workshop Artificial Intelligence and Statistics, 1999.

[21]

M. Claypool A. Gokhale T. Miranda P. Murnikov D. Netes and M. Sartin, “Combining Content-Based and Collaborative Filters in an Online Newspaper,” Proc. ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation, Aug. 1999.

[22]

W.W. Cohen R.E. Schapire and Y. Singer, “Learning to Order Things,” J. Artificial Intelligence Research, vol. 10, pp. 243-270, 1999.

Digital Library

[23]

D. Cohn L. Atlas and R. Ladner, “Improving Generalization with Active Learning,” Machine Learning, vol. 15, no. 2, pp. 201-221, 1994.

[24]

D. Cohn Z. Ghahramani and M. Jordan, “Active Learning with Statistical Models,” J. Artificial Intelligence Research, vol. 4, pp. 129-145, 1996.

Digital Library

[25]

M. Condliff D. Lewis D. Madigan and C. Posse, “Bayesian Mixed-Effects Models for Recommender Systems,” Proc. ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation, Aug. 1999.

[26]

C. Cortes K. Fisher D. Pregibon A. Rogers and F. Smith, “Hancock: A Language for Extracting Signatures from Data Streams,” Proc. Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2000.

Digital Library

[27]

J. Delgado and N. Ishii, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” Proc. ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation, 1999.

[28]

C. Dellarocas, “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,” Management Science, vol. 49, no. 10, pp. 1407-1424, 2003.

Digital Library

[29]

M. Deshpande and G. Karypis, “Item-Based Top-N Recommendation Algorithms,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 143-177, 2004.

Digital Library

[30]

J. Duchon, “Splines Minimizing Rotation-Invariate Semi-Norms in Sobolev Spaces,” Constructive Theory of Functions of Several Variables, W. Schempp and Zeller, ed., pp. 85-100, Springer, 1979.

[31]

R.O. Duda P.E. Hart and D.G. Stork, Pattern Classification. John Wiley & Sons, 2001.

Digital Library

[32]

F.R. Dwyer, “Customer Lifetime Valuation to Support Marketing Decision Making,” J. Direct Marketing, vol. 3, no. 4, 1989.

[33]

M. Ehrgott, Multicriteria Optimization. Springer Verlag, Sept. 2000.

Digital Library

[34]

T. Fawcett and F. Provost, “Combining Data Mining and Machine Learning for Efficient User Profiling,” Proc. Second Int'l Conf. Knowledge Discovery and Data Mining (KDD-96), 1996.

Digital Library

[35]

Y. Freund R. Iyer R.E. Schapire and Y. Singer, “An Efficient Boosting Algorithm for Combining Preferences,” Proc. 15th Int'l Conf. Machine Learning, 1998.

Digital Library

[36]

Y. Freund H.S. Seung E. Shamir and N. Tishby, “Selective Sampling Using the Query by Committee Algorithm,” Machine Learning, vol. 28, nos. 2-3, pp. 133-168, 1997.

Digital Library

[37]

L. Getoor and M. Sahami, “Using Probabilistic Relational Models for Collaborative Filtering,” Proc. Workshop Web Usage Analysis and User Profiling (WEBKDD '99), Aug. 1999.

[38]

D. Goldberg D. Nichols B.M. Oki and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry,” Comm. ACM, vol. 35, no. 12, pp. 61-70, 1992.

Digital Library

[39]

K. Goldberg T. Roeder D. Gupta and C. Perkins, “Eigentaste: A Constant Time Collaborative Filtering Algorithm,” Information Retrieval J., vol. 4, no. 2, pp. 133-151, July 2001.

Digital Library

[40]

N. Good J.B. Schafer J.A. Konstan A. Borchers B. Sarwar J.L. Herlocker and J. Riedl, “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI-99), pp. 439-446, July 1999.

Digital Library

[41]

J.L. Herlocker J.A. Konstan A. Borchers and J. Riedl, “An Algorithmic Framework for Performing Collaborative Filtering,” Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '99), 1999.

Digital Library

[42]

J.L. Herlocker J.A. Konstan and J. Riedl, “Explaining Collaborative Filtering Recommendations,” Proc. ACM Conf. Computer Supported Cooperative Work, 2000.

Digital Library

[43]

J.L. Herlocker and J.A. Konstan, “Content-Independent Task-Focused Recommendation,” IEEE Internet Computing, vol. 5, no. 6, pp. 40-47, Nov./Dec. 2001.

Digital Library

[44]

J.L. Herlocker J.A. Konstan L.G. Terveen and J.T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.

Digital Library

[45]

W. Hill L. Stead M. Rosenstein and G. Furnas, “Recommending and Evaluating Choices in a Virtual Community of Use,” Proc. Conf. Human Factors in Computing Systems, 1995.

Digital Library

[46]

T. Hofmann, “Probabilistic Latent Semantic Analysis,” Proc. 15th Conf. Uncertainty in Artificial Intelligence, pp. 289-296, 1999.

Digital Library

[47]

T. Hofmann, “Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis,” Proc. 26th Ann. Int'l ACM SIGIR Conf., 2003.

Digital Library

[48]

T. Hofmann, “Latent Semantic Models for Collaborative Filtering,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 89-115, 2004.

Digital Library

[49]

Z. Huang H. Chen and D. Zeng, “Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 116-142, 2004.

Digital Library

[50]

D.A. Hull, “The TREC-7 Filtering Track: Description and Analysis,” Proc. Seventh Text Retrieval Conf. (TREC-7), 1999.

[51]

R. Jin L. Si and C. Zhai, “Preference-Based Graphic Models for Collaborative Filtering,” Proc. 19th Conf. Uncertainty in Artificial Intelligence (UAI 2003), Aug. 2003a.

Digital Library

[52]

R. Jin L. Si C. Zhai and J. Callan, “Collaborative Filtering with Decoupled Models for Preferences and Ratings,” Proc. 12th Int'l Conf. Information and Knowledge Management (CIKM 2003), Nov. 2003b.

Digital Library

[53]

J.A. Konstan B.N. Miller D. Maltz J.L. Herlocker L.R. Gordon and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Comm. ACM, vol. 40, no. 3, pp. 77-87, 1997.

Digital Library

[54]

J.A. Konstan J. Riedl A. Borchers and J.L. Herlocker, “Recommender Systems: A GroupLens Perspective,” Proc. Recommender Systems, Papers from 1998 Workshop, Technical Report WS-98-08, 1998.

[55]

R. Kumar P. Raghavan S. Rajagopalan and A. Tomkins, “Recommendation Systems: A Probabilistic Analysis,” J. Computer and System Sciences, vol. 63, no. 1, pp. 42-61, 2001.

Digital Library

[56]

K. Lang, “Newsweeder: Learning to Filter Netnews,” Proc. 12th Int'l Conf. Machine Learning, 1995.

[57]

W.S. Lee, “Collaborative Learning for Recommender Systems,” Proc. Int'l Conf. Machine Learning, 2001.

[58]

D. Lewis and J. Catlett, “Heterogeneous Uncertainty Sampling for Supervised Learning,” Proc. 11th Int'l Conf. Machine Learning, pp.nbsp148-156, 1994.

[59]

J. Li and O.R. Zaïane, “Combining Usage, Content, and Structure Data to Improve Web Site Recommendation,” Proc. Fifth Int'l Conf. Electronic Commerce and Web Technologies (EC-Web '04), pp. 305-315, 2004.

[60]

G.L. Lilien P. Kotler and K.S. Moorthy, Marketing Models. Prentice Hall, 1992.

[61]

G. Linden B. Smith and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Jan./Feb. 2003.

Digital Library

[62]

N. Littlestone and M. Warmuth, “The Weighted Majority Algorithm,” Information and Computation, vol. 108, no. 2, pp. 212-261, 1994.

Digital Library

[63]

H. Mannila H. Toivonen and A.I. Verkamo, “Discovering Frequent Episodes in Sequences,” Proc. First Int'l Conf. Knowledge Discovery and Data Mining (KDD-95), 1995.

[64]

B. Marlin, “Modeling User Rating Profiles for Collaborative Filtering,” Proc. 17th Ann. Conf. Neural Information Processing Systems (NIPS '03), 2003.

[65]

P. Melville R.J. Mooney and R. Nagarajan, “Content-Boosted Collaborative Filtering for Improved Recommendations,” Proc. 18th Nat'l Conf. Artificial Intelligence, 2002.

Digital Library

[66]

S.E. Middleton N.R. Shadbolt and D.C. de Roure, “Ontological User Profiling in Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 54-88, 2004.

Digital Library

[67]

B.N. Miller I. Albert S.K. Lam J.A. Konstan and J. Riedl, “MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System,” Proc. Int'l Conf. Intelligent User Interfaces, 2003.

Digital Library

[68]

B. Mobasher H. Dai T. Luo and M. Nakagawa, “Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization,” Data Mining and Knowledge Discovery, vol. 6, no. 1, pp. 61-82, 2002.

Digital Library

[69]

R.J. Mooney and L. Roy, “Content-Based Book Recommending Using Learning for Text Categorization,” Proc. ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation, 1999.

[70]

R.J. Mooney P.N. Bennett and L. Roy, “Book Recommending Using Text Categorization with Extracted Information,” Proc. Recommender Systems Papers from 1998 Workshop, Technical Report WS-98-08, 1998.

[71]

B.P.S. Murthi and S. Sarkar, “The Role of the Management Sciences in Research on Personalization,” Management Science, vol. 49,no. 10, pp. 1344-1362, 2003.

Digital Library

[72]

A. Nakamura and N. Abe, “Collaborative Filtering Using Weighted Majority Prediction Algorithms,” Proc. 15th Int'l Conf. Machine Learning, 1998.

Digital Library

[73]

G. Nurnberger, Approximation by Spline Functions. Springer-Verlag, 1989.

[74]

D.W. Oard and J. Kim, “Implicit Feedback for Recommender Systems,” Proc. Recommender Systems. Papers from 1998 Workshop, Technical Report WS-98-08, 1998.

[75]

D. Pavlov and D. Pennock, “A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains,” Proc. 16th Ann. Conf. Neural Information Processing Systems (NIPS '02), 2002.

[76]

M. Pazzani, “A Framework for Collaborative, Content-Based, and Demographic Filtering, Artificial Intelligence Rev., pp. 393-408, Dec. 1999.

Digital Library

[77]

M. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, pp. 313-331, 1997.

Digital Library

[78]

C.C. Peddy and D. Armentrout, Building Solutions with Microsoft Commerce Server 2002. Microsoft Press, 2003.

Digital Library

[79]

D.M. Pennock and E. Horvitz, “Collaborative Filtering by Personality Diagnosis: A Hybrid Memory And Model-Based Approach,” Proc. Int'l Joint Conf. Artificial Intelligence Workshop: Machine Learning for Information Filtering, Aug. 1999.

[80]

A. Popescul L.H. Ungar D.M. Pennock and S. Lawrence, “Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments,” Proc. 17th Conf. Uncertainty in Artificial Intelligence, 2001.

Digital Library

[81]

M.J.D. Powell, Approximation Theory and Methods. Cambridge Univ. Press, 1981.

[82]

N. Ramakrishnan B.J. Keller B.J. Mirza A.Y. Grama and G. Karypis, “Privacy Risks in Recommender Systems,” IEEE Internet Computing, vol. 5, no. 6, pp. 54-62, Nov./Dec. 2001.

Digital Library

[83]

A.M. Rashid I. Albert D. Cosley S.K. Lam S.M. McNee J.A. Konstan and J. Riedl, “Getting to Know You: Learning New User Preferences in Recommender Systems,” Proc. Int'l Conf. Intelligent User Interfaces, 2002.

Digital Library

[84]

S. Robertson and S. Walker, “Threshold Setting in Adaptive Filtering,” J. Documentation, vol. 56, pp. 312-331, 2000.

[85]

J.J. Rocchio, “Relevance Feedback in Information Retrieval,” SMART Retrieval System-Experiments in Automatic Document Processing, G. Salton, ed., chapter 14, Prentice Hall, 1971.

[86]

P. Resnick N. Iakovou M. Sushak P. Bergstrom and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. 1994 Computer Supported Cooperative Work Conf., 1994.

Digital Library

[87]

E. Rich, “User Modeling via Stereotypes,” Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.

[88]

S. Rosset E. Neumann U. Eick N. Vatnik and Y. Idan, “Customer Lifetime Value Modeling and Its Use for Customer Retention Planning,” Proc. Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD-2002), July 2002.

Digital Library

[89]

G. Salton, Automatic Text Processing. Addison-Wesley, 1989.

Digital Library

[90]

B. Sarwar G. Karypis J. Konstan and J. Riedl, “Application of Dimensionality Reduction in Recommender Systems-A Case Study,” Proc. ACM WebKDD Workshop, 2000.

[91]

B. Sarwar G. Karypis J. Konstan and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” Proc. 10th Int'l WWW Conf., 2001.

Digital Library

[92]

R. Schaback and H. Wendland, “Characterization and Construction of Radial Basis Functions,” Multivariate Approximation and Applications, N. Dyn, D. Leviatan, D. Levin, and A. Pinkus, eds., Cambridge Univ. Press, 2001.

[93]

J.B. Schafer J.A. Konstan and J. Riedl, “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery, vol. 5, nos. 1/2, pp. 115-153, 2001.

Digital Library

[94]

A.I. Schein A. Popescul L.H. Ungar and D.M. Pennock, “Methods and Metrics for Cold-Start Recommendations,” Proc. 25th Ann. Int'l ACM SIGIR Conf., 2002.

Digital Library

[95]

D.C. Schmittlein D.G. Morrison and R. Colombo, “Counting Your Customers: Who Are They and What Will They Do Next?” Management Science, vol. 33, no. 1, 1987.

Digital Library

[96]

G. Shani R. Brafman and D. Heckerman, “An MDP-Based Recommender System,” Proc. 18th Conf. Uncertainty in Artificial Intelligence, Aug. 2002.

Digital Library

[97]

U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” Proc. Conf. Human Factors in Computing Systems, 1995.

Digital Library

[98]

B. Sheth and P. Maes, “Evolving Agents for Personalized Information Filtering,” Proc. Ninth IEEE Conf. Artificial Intelligence for Applications, 1993.

[99]

L. Si and R. Jin, “Flexible Mixture Model for Collaborative Filtering,” Proc. 20th Int'l Conf. Machine Learning, Aug. 2003.

[100]

I. Soboroff and C. Nicholas, “Combining Content and Collaboration in Text Filtering,” Proc. Int'l Joint Conf. Artificial Intelligence Workshop: Machine Learning for Information Filtering, Aug. 1999.

[101]

G. Somlo and A. Howe, “Adaptive Lightweight Text Filtering,” Proc. Fourth Int'l Symp. Intelligent Data Analysis, 2001.

Digital Library

[102]

R.B. Statnikov and J.B. Matusov, Multicriteria Optimization and Engineering. Chapman & Hall, 1995.

[103]

L. Terveen W. Hill B. Amento D. McDonald and J. Creter, “PHOAKS: A System for Sharing Recommendations,” Comm. ACM, vol. 40, no. 3, pp. 59-62, 1997.

Digital Library

[104]

T. Tran and R. Cohen, “Hybrid Recommender Systems for Electronic Commerce,” Proc. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, AAAI Press, 2000.

[105]

L.H. Ungar and D.P. Foster, “Clustering Methods for Collaborative Filtering,” Proc. Recommender Systems, Papers from 1998 Workshop, Technical Report WS-98-08 1998.

[106]

W. Wade, “A Grocery Cart that Holds Bread, Butter, and Preferences,” New York Times, Jan. 16, 2003.

[107]

Y. Yang and B. Padmanabhan, “On Evaluating Online Personalization,” Proc. Workshop Information Technology and Systems, pp.nbsp35-41, Dec. 2001.

[108]

K. Yu X. Xu J. Tao M. Ester and H.-P. Kriegel, “Instance Selection Techniques for Memory-Based Collaborative Filtering,” Proc. Second SIAM Int'l Conf. Data Mining (SDM '02), 2002.

[109]

K. Yu A. Schwaighofer V. Tresp X. Xu and H.-P. Kriegel, “Probabilistic Memory-Based Collaborative Filtering,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 1, pp. 56-69, Jan. 2004.

Digital Library

[110]

Proc. WEBKDD 2002-Mining Web Data for Discovering Usage Patterns and Profiles, O.R. Zaïane, J. Srivastava, M. Spiliopoulou, B.nbspM. Masand, eds., 2003.

[111]

Y. Zhang and J. Callan, “Maximum Likelihood Estimation for Filtering Thresholds,” Proc. 24th Ann. Int'l ACM SIGIR Conf., 2001.

Digital Library

[112]

Y. Zhang J. Callan and T. Minka, “Novelty and Redundancy Detection in Adaptive Filtering,” Proc. 25th Ann. Int'l ACM SIGIR Conf., pp. 81-88, 2002.

Digital Library

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Index Terms

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
1. Applied computing
  1. Education
    1. Collaborative learning
2. Human-centered computing
  1. Collaborative and social computing
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 17, Issue 6

June 2005

144 pages

ISSN:1041-4347

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IEEE Educational Activities Department

United States

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Published: 01 June 2005

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https://dl.acm.org/doi/10.1177/00375497231214563
Aridor GGoncalves DKong RKluver DKonstan J(2024)The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688158(1-1)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688158
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