Developing recommender systems with the consideration of product profitability for sellers

Information Sciences

Volumn 178, Issue 4, 2008, Pages 1032-1048

  Chen, Long Sheng ^a   Hsu, Fei Hao ^b   Chen, Mu Chen ^c   Hsu, Yuan Chia ^d  

^a CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^c NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^d Inotera Memories   (Taiwan)

Author keywords

Collaborative filtering; Cross selling; Electronic commerce; Personalization; Product profitability; Recommender systems

Indexed keywords

COLLABORATIVE FILTERING; PERSONALIZATION; PRODUCT PROFITABILITY; PROFITABILITY FACTOR; RECOMMENDATION ACCURACY; RECOMMENDER SYSTEMS;

CUSTOMER SATISFACTION; PURCHASING; WEBSITES;

ELECTRONIC COMMERCE;

EID: 36549076978     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.09.027     Document Type: Article

References (54)

1. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington DC, USA, 1993, pp. 207-216.
2. Ansari A., Essegaier S., and Kohli R. Internet recommendation systems. Journal of Marketing Research 37 3 (2000) 363-375
3. Balabanovic M., and Shoham Y. Combining content-based and collaborative recommendation. Communications of the ACM 40 3 (1997) 66-72
4. K. Bradley, B. Smyth, Improving recommendation diversity, in: Proceedings of the 12th Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, 2001.
5. J. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-52.
6. Changchiena S.W., Lee C.-F., and Hsu Y.-J. On-line personalized sales promotion in electronic commerce. Expert Systems with Applications 27 1 (2004) 35-52
7. Cheng P.J., and Yang W.P. A new content-based access method for video databases. Information Sciences 118 1-4 (1999) 37-73
8. Cheung K.-W., Kwok J.T., Law M.H., and Tsui K.-C. Mining customer product ratings for personalized marketing. Decision Support Systems 35 2 (2003) 231-243
9. Cho Y.H., and Kim J.K. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications 26 2 (2004) 233-246
10. Cho Y.H., Kim J.K., and Kim S.H. A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications 23 3 (2002) 329-342
11. Cornelis C., Lu J., Guo X., and Zhang G. One-and-only item recommendation with fuzzy logic techniques. Information Sciences 177 22 (2007) 4906-4921
12. Deshpande M., and Karypis G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22 1 (2004) 143-177
13. R. Ghani, A. Fano, Building recommender systems using a knowledge base of product semantics, in: Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, at the 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, 2002.
14. Goldberg D., Nichols D., Oki B.M., and Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM 35 12 (1992) 61-70
15. Han P., Xie B., Yang F., and Shen R. A scalable P2P recommender system based on distributed collaborative filtering. Expert Systems with Applications 27 2 (2004) 203-210
16. J. Herlocker, J. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in: Proceedings of the 1999 Conference on Research and Development in Information Retrieval, August 1999.
17. Hwang C.-S., and Tsai P.-J. A collaborative recommender system based on user association clusters. Lecture Note on Computer Science 3806 (2005) 463-469
18. Jin R., Chai J.Y., and Si L. An automatic weighting scheme for collaborative filtering. Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval (2004), ACM Press, Sheffield, United Kingdom 337-344
19. Kaufman L., and Rousseeuw P.J. Finding Groups in Data, An Introduction to Cluster Analysis (1990), Wiley, New York
20. Kazienko P., and Adamski M. AdROSA-adaptive personalization of web advertising. Information Sciences 177 11 (2007) 2269-2295
21. Kim J.K., Cho Y.H., Kim W.J., Kim J.R., and Suh J.H. A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications 1 3-4 (2002) 301-313
22. Kim J., and Lee E. Semantic web recommender systems based personalization service for user XQuery pattern. Lecture Notes on Computer Science 3828 (2005) 848-857
23. Kim B.M., Li Q., Park C.S., and Kim S. A new approach for combining content-based and collaborative filters. Journal of Intelligence Information Systems 27 (2006) 79-91
24. Kohrs A., and Merialdo B. Creating user-adapted websites by the use of collaborative filtering. Interacting with Computers 13 6 (2001) 695-716
25. Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., and Riedl J. GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40 3 (1997) 77-87
26. B. Krulwich, C. Burkey, Learning user information interests through extraction of semantically significant phrases, in: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, 1996.
27. Kuo Y.-F., and Chen L.-S. Personalization technology application to internet content provider. Expert Systems with Applications 21 4 (2001) 203-215
28. K. Lang, NewsWeeder: learning to filter netnews, in: Proceedings of the Twelfth International Conference on Machine Learning, 1995, pp. 331-339.
29. Lawrence R.D., Almasi G.S., Kotlyar V., Viveros M.S., and Duri S. Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery 5 1/2 (2001) 11-32
30. Lee D.-S., Kim G.-Y., and Choi H.-I. A web-based collaborative filtering system. Pattern Recognition 36 2 (2003) 519-526
31. Lee W.-P., Liu C.-H., and Lu C.-C. Intelligent agent-based systems for personalized recommendations in internet commerce. Expert Systems with Applications 22 (2002) 275-284
32. Lingras P., Hogo M., Snorek M., and West C. Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. Information Sciences 172 1-2 (2005) 215-240
33. P. Melville, R.J. Mooney, R. Nagarajan, Content-boosted collaborative filtering for improved recommendations, in: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), Edmonton, Canada, 2002, pp. 187-192.
34. Mild A., and Reutterer T. An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services 10 3 (2003) 123-133
35. Min S.-H., and Han I. Detection of the customer time-variant pattern for improving recommender systems. Expert Systems with Applications 28 2 (2005) 189-199
36. Montaner M., López B., and de la Rosa J.L. A taxonomy of recommender agents on the Internet. Artificial Intelligence Review 19 4 (2003) 285-330
37. Reichheld F.F. Loyalty-based management. Harvard Business Review 71 2 (1993) 64-73
38. Reichheld F.F., and Sasser W.E. Zero defections: quality comes to services. Harvard Business Review 68 5 (1990) 105-111
39. Resnick P., and Varian H.R. Recommender systems. Communications of the ACM 40 3 (1997) 56-58
40. Rucker J., and Polenco M.J. Siteseer: personalized navigation for the web. Communications of the ACM 40 3 (1997) 73-76
41. B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Analysis of recommendation algorithms for e-commerce, in: Proceedings of the 2nd ACM Conference on Electronic Commerce, 2000, pp. 158-167.
42. B. Sarwar, G. Karypis, J. Konstan, J. Reidl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285-295.
43. J.B. Schafer, J. Konstan, J. Riedi, Recommender systems in e-commerce, in: Proceedings of the 1st ACM Conference on Electronic Commerce, 1999, pp. 158-166.
44. A.I. Schein, A. Popescul, L.H. Ungar, D.M. Pennock, Methods and metrics for cold-start recommendations, in: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Tampere, Finland, 2002.
45. U. Shardanand, P. Maes, Social information filtering: algorithm for automating word of mouth, in: Proceedings of International Conference on Human Factors in Computing Systems, 1995, pp. 210-217.
46. Terveen L., Hill W., Armento B., McDonald D., and Creter J. Phoaks: a system for sharing recommendations. Communications of the ACM 40 3 (1997) 59-62
47. K. Tso, L. Schmidt-Thieme, Attribute-aware collaborative filtering, in: Proceedings of 29th Annual Conference of the German Classification Society, Magdeburg, Germany, 2005.
48. Vozalis M.G., and Margaritis K.G. Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Information Sciences 177 15 (2007) 3017-3037
49. Xie B., Han P., Yang F., Shen R.-M., Zeng H.-J., and Chen Z. DCFLA: A distributed collaborative-filtering neighbor-locating algorithm. Information Sciences 177 6 (2007) 1349-1363
50. P.S. Yu, Data mining and personalization technologies, in: Proceedings of the Sixth International Conference on Database Systems for Advanced Applications, 1999, pp. 6-13.
51. Yu J.X., Chong Z., Lu H., Zhang Z., and Zhou A. A false negative approach to mining frequent item sets from high speed transactional data streams. Information Sciences 176 14 (2006) 1986-2015
52. Yun U. Efficient mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177 17 (2007) 3477-3499
53. Zhang S., Zhang J., and Zhang C. EDUA: An efficient algorithm for dynamic database mining. Information Sciences 177 13 (2007) 2756-2767
54. Zhong S. Privacy-preserving algorithms for distributed mining of frequent item sets. Information Sciences 177 2 (2007) 490-503