A New Approach for Recommender System
Pages 35 - 39
Abstract
In today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according to the preference ratings of the similar users. However, if the preference ratings of the target user are rare or none, then it cannot effectively identify the users with the similar preferences to the target user.
In order to solve the problem of collaborative filtering, this study uses the implicit rating method to automatically calculate the user preference for the items by using the transaction data of the users, and further constructs an item-to-item, user-to-user, and user-to-item relationships, which can be used to calculate the preference rating for the target user, and recommend the products to the target user. The experimental results also show that the recommendation accuracy of our algorithm is higher than the other algorithms on average.
References
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Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 43--52.
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Billsus, D. and Pazzani, M. J. 1998. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning, 46--54.
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He, J. and Chu, W. W. 2010. A social network-based recommender system (SNRS). Data Mining for Social Network Data, vol. 12 of the series Annals Information Systems, 47--74.
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Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings. of ACM SIGIR conference on Research and development in information retrieval, 230--237.
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Herlocker, J., Konstan, J. A., and Riedl. J. 2002. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information retrieval, 287--310.
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Koren, Y., et al. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 4, no. 1, Article 1.
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Lathia, N., Hailes, S. and Capra, L. 2008. kNN CF: a temporal social network. In Proceedings of the 2008 ACM conference on Recommender systems, 227--234.
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Lemire, D. and Maclachlan, A. 2005. Slope One Predictors for Online Rating-Based Collab- orative Filtering. In SIAM Data Mining (SDM'05), Newport Beach, California, April 21-23.
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Papagelis, M. and Plexousakis, D. 2005. Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence, 781--789.
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Resnick, P., Lacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. GroupLens: an open architecture for collaborative filtering of netnews. Proc. of ACM conference on Computer supported cooperative work, 175--186.
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Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2001. Item-based collaborative filtering recommend- ation algorithms. In Proceedings of the 10th Int. Conf. on World Wide Web, 285--295.
Index Terms
- A New Approach for Recommender System
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Recommender system architecture based on Mahout and a main memory database
In this study, we propose a news recommendation system architecture using a main memory database (DB) and Mahout. The user's news preference rate is calculated automatically based on the time the user spends reading news items and their length. While ...
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis
Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from ...
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Published In
August 2017
117 pages
ISBN:9781450352840
DOI:10.1145/3127942
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Association for Computing Machinery
New York, NY, United States
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Published: 10 August 2017
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ICACS '17
ICACS '17: 2017 International Conference on Algorithms, Computing and Systems
August 10 - 13, 2017
Jeju Island, Republic of Korea
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