Top k recommendations using contextual conditional preferences model
2017 Federated Conference on Computer Science and Information Systems (FedCSIS)(2017)
摘要
Recommender systems are software tools and techniques which aim at suggesting to users items they might be interested in. Context-aware recommender systems are a particular category of recommender systems which exploit contextual information to provide more adequate recommendations. However, recommendation engines still suffer from the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we introduce a method for generating a list of top k recommendations in a new user cold-start situations. It is based on a user model called Contextual Conditional Preferences and utilizes a satisfiability measure proposed in this paper. We analyze accuracy measures as well as serendipity, novelty and diversity of results obtained using three context-aware publicly available datasets in comparison with several contextual and traditional state-of-the-art baselines. We show that our method is applicable in the new user cold-start situations as well as in typical scenarios.
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关键词
cold-start problem,context-aware publicly available datasets,contextual state-of-the-art baselines,contextual conditional preferences model,software tools,context-aware recommender systems,contextual information,recommendation engines,top k recommendations,new user cold-start situations,satisfiability measure
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