Gang Zhao
ABSTRACT
The development of Web 2.0 technology has led to huge economic benefits and challenges for both e-commerce websites and online shoppers. One core technology to increase sales and consumers' satisfaction is the use of recommender systems. Existing product recommender systems consider the order of items purchased by users to obtain a list of recommended items. However, they do not consider the time interval between the products purchased. For example, there is often an interval of 2-3 months between the purchase of printer ink cartridges or refills. Thus, recommending appropriate ink cartridges one week before the user needs to replace the depleted ink cartridges would increase the likelihood of a purchase decision. In this paper, we propose to utilize the purchase interval information to improve the performance of the recommender systems for e-commerce. We design an efficient algorithm to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model. We evaluate our approach on a real world ecommerce dataset. Experimental results demonstrate that our approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms.
- W.J. Baumol and A.S. Blinder. Microeconomics: Principles and Policy. 2008.Google Scholar
- J.S. Breese, D. Heckerman, and C. Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Microsoft Technical Report, pages 43--52, 2007.Google Scholar
- C. Cobb and P. Douglas. Latent semantic models for collaborative filtering. American Economic Review, 18:139--165, 1928.Google Scholar
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4:133--151, 2001. Google ScholarDigital Library
- J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl. Evaluating collaborative filtering recommender systems. ACM TOIS, 22, 2004. Google ScholarDigital Library
- T. Hofmann. Latent semantic models for collaborative filtering. ACM TOIS, 22:89--115, 2004. Google ScholarDigital Library
- http://news.imeigu.com/a/1315461895947.html. Market share and sales growth situation of jingdong mall., September 2011.Google Scholar
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In ACM SIGKDD, pages 426--434, 2008. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. pages 89--97, 2009.Google Scholar
- N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In ACM SIGIR, pages 210--217, 2010. Google ScholarDigital Library
- B. Li, A. Ghose, and P.G. Ipeirotis. Towards a theory model for product search. In WWW Conference, pages 327--336, 2011. Google ScholarDigital Library
- M.R. McLaughlin and J.L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In ACM SIGIR, pages 329--336, 2004. Google ScholarDigital Library
- P. Melville, R.J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In National Conference on Artificial intelligence, pages 187--192, 2002. Google ScholarDigital Library
- K. Miyahara and M.J. Pazzani. Collaborative filtering with the simple bayesian classifier. In PRICAI, pages 679--689, 2000. Google ScholarDigital Library
- D.Y. Pavlov and D.M. Pennock. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. In Neural Information Processing Systems, pages 1441--1448, 2002.Google Scholar
- S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW Conference, pages 811--820, 2010. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In WWW Conference, pages 285--295, 2001. Google ScholarDigital Library
- S.H. Seng, S. Chee, J. Han, and K. Wang. Rectree: An efficient collaborative filtering method. In DaWaK, pages 141--151, 2001. Google ScholarDigital Library
- L. Si and R. Jin. Flexible Mixture Model for Collaborative Filtering. In ICML, pages 704--711, 2003.Google ScholarDigital Library
- X. Su and T.M. Khoshgoftaar. Collaborative filtering for multi-class data using belief nets algorithms. In IEEE ICTAI, pages 497--504, 2006. Google ScholarDigital Library
- J. Wang and Y. Zhang. Utilizing marginal net utility for recommendation in e-commerce. In ACM SIGIR, pages 1003--1012, 2011. Google ScholarDigital Library
- L. Xiang, Q. Yuan, and S. et. al. Zhao. Temporal recommendation on graphs via long- and short-term preference fusion. In ACM SIGKDD, pages 723--732, 2010. Google ScholarDigital Library
Index Terms
- Increasing temporal diversity with purchase intervals
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