CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling multiple users' purchase over a single account for collaborative filtering
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Challenge on context-aware movie recommendation: CAMRa2011
Proceedings of the fifth ACM conference on Recommender systems
Temporal rating habits: a valuable tool for rating discrimination
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Identifying users from their rating patterns
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Mining relational context-aware graph for rater identification
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Exploiting time contexts in collaborative filtering: an item splitting approach
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Probabilistic group recommendation via information matching
Proceedings of the 22nd international conference on World Wide Web
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Popular online rental services such as Netflix and MoviePilot often manage household accounts. A household account is usually shared by various users who live in the same house, but in general does not provide a mechanism by which current active users are identified, and thus leads to considerable difficulties for making effective personalized recommendations. The identification of the active household members, defined as the discrimination of the users from a given household who are interacting with a system (e.g. an on-demand video service), is thus an interesting challenge for the recommender systems research community. In this paper, we formulate the above task as a classification problem, and address it by means of global and local feature selection methods and classifiers that only exploit time features from past item consumption records. The results obtained from a series of experiments on a real dataset show that some of the proposed methods are able to select relevant time features, which allow simple classifiers to accurately identify active members of household accounts.