A New Temporal Recommendation System based on Users’ Similarity Prediction

Nima Joorabloo, Mahdi Jalili, Yongli Ren


Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar users to provide suggestions for a target user. Users’ preferences often vary over time and many traditional collaborative filtering algorithms fail to consider this important issue. In this paper, a novel recommendation method is proposed based on predicting similarity between users in the future and forecasting their similarity trends over time. The proposed method uses the sequence of users’ ratings to predict the similarities between users in the future and use the predicted similarities instead of the original ones to detect users’ neighbours. Experimental results on benchmark datasets show that the proposed method significantly outperforms classical and state-of-the-art recommendation methods.


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