ISeller: A Flexible Personalization Infrastructure for e-Commerce Applications
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Harnessing geo-tagged resources for Web personalization
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Information Sciences: an International Journal
Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Contents Recommendation Method Using Social Network Analysis
Wireless Personal Communications: An International Journal
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Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems. Algorithms of this type derive personalized product propositions for customers by exploitingstatistics derived from vast amounts of transaction data.Traditionally, basic CF algorithms have exploited a single category of ratings despite the fact that on many platforms a variety of different forms of user feedback are available for personalization and recommendation. In this paper we explore a collaborative feature-combination algorithm that concurrently exploits multiple aspects of the user model like clickstream data, sales transactions and explicit user requirements to overcome some known shortcomingsof CF like the cold-start problem for new users. We validate our contribution by evaluating it against the standard user-to-user CF algorithm using a dataset from a commercial Web shop. Evaluation results indicate considerable improvements in terms of user coverageand accuracy.