Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach.

• A probabilistic model is proposed to evaluate user's rating profile. • Temporal reliability and confidence measures are used to design recommender systems. • Changing of users' preferences over time is considered in the proposed method. • The reliability measure is used to evaluate the quality of the predictions. • The confidence measure is used to identify ineffective users from neighbors set. Recommender systems use intelligent algorithms to learn a user's preferences and provide them relevant suggestions. Lack of sufficient ratings – also known as data sparsity problem – often results in poor recommendation performance. The existing recommendation methods have mainly focused on designing recommenders with high accuracy without paying much attention to the reliability of the recommendations. On the other hand, the users' preferences may vary over time and considering the time factor in the design process is crucial, which has been largely ignored in most of the existing recommenders. To deal with these issues, a novel recommendation method is proposed in this paper which incorporates temporal reliability and confidence measures into the recommendation process. First, the effectiveness of the users' rating is measured using a probabilistic approach and ineffective rating profiles are enriched by adding some implicit ratings to them. The quality of the predictions is evaluated using a temporal reliability measure taking into account the changes of users' preferences over time. Then, the ratings with low reliability values are recalculated using a novel procedure, which updates the target user's neighborhood by removing ineffective users. This leads to a temporal confidence measure that is used to update the neighborhood to provide more reliable and accurate recommendations. The superiority of the proposed method over state-of-the-art recommendation methods is shown by conducting extensive experiments on three benchmark datasets. [ABSTRACT FROM AUTHOR]