GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
IEEE Transactions on Knowledge and Data Engineering
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
Influences of customer preference development on the effectiveness of recommendation strategies
Electronic Commerce Research and Applications
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Many e-commerce sites employ collaborative filtering techniques to provide recommendations to customers based on the preferences of similar users. However, as the number of customers and products increases, the prediction accuracy of collaborative filtering algorithms declines because of sparse ratings. In addition, the traditional recommendation approaches just consider the item's attributes and the preference similarities between users; however, they are not concerned that users' preferences may be developed as their familiarity with or experiences during choice or preference elicitation grows. In this work, we propose an anchor-based hybrid filtering approach to capture the user's preferences of movie genres interactively and then achieve precise recommendations. To conduct this experiment, we recruited 30 users with different types of preference stabilities for movie genres. The experimental results show that the proposed anchor-based hybrid filtering approach can effectively filter out the users' undesired movie genres, especially for the user who has unstable movie genre preferences. The results suggest that the factor of the stability of users' preferences can be considered for developing effective recommendation strategies.