Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information

Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.

[1]  Eric Horvitz,et al.  Decision theory in expert systems and artificial intelligenc , 1988, Int. J. Approx. Reason..

[2]  Naoki Abe,et al.  Collaborative Filtering Using Weighted Majority Prediction Algorithms , 1998, ICML.

[3]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[4]  Qing Li,et al.  A new approach for combining content-based and collaborative filters , 2006, Journal of Intelligent Information Systems.

[5]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[6]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

[7]  C. Lee Giles,et al.  A system for automatic personalized tracking of scientific literature on the Web , 1999, DL '99.

[8]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[9]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[10]  M. Lynn.  Hawaii International Conference on System Sciences , 1996 .

[11]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[12]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[13]  Eric Horvitz,et al.  Decision theory in expert systems and artificial intelligenc , 1988, Int. J. Approx. Reason..

[14]  Scott M. Smith,et al.  Computer Intensive Methods for Testing Hypotheses: An Introduction , 1989 .

[15]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[16]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[17]  Thorsten von Eicken,et al.  技術解説 IEEE Computer , 1999 .

[18]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[19]  James Rucker,et al.  Siteseer: personalized navigation for the Web , 1997, CACM.

[20]  D. Heckerman,et al.  Toward Normative Expert Systems: Part I The Pathfinder Project , 1992, Methods of Information in Medicine.

[21]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[22]  Eric Horvitz,et al.  Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering , 2000, AAAI/IAAI.

[23]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[24]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[25]  T. Saaty.  Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[26]  Ronald A. Howard,et al.  The Foundations of Decision Analysis , 1968, IEEE Trans. Syst. Sci. Cybern..

[27]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[28]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[29]  Duen-Ren Liu,et al.  Integrating AHP and data mining for product recommendation based on customer lifetime value , 2005, Inf. Manag..

[30]  K. J. Evans,et al.  Computer Intensive Methods for Testing Hypotheses: An Introduction , 1990 .

[31]  Michael J. Shaw,et al.  Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts , 2001, Int. J. Electron. Commer..

[32]  Moshe Ben-Bassat,et al.  Myopic Policies in Sequential Classification , 1978, IEEE Transactions on Computers.

[33]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[34]  C. Lee Giles,et al.  Digital Libraries and Autonomous Citation Indexing , 1999, Computer.

[35]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[36]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .