Abstract
As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.
Highlights
The existing recommendation systems have adopted various methods to derive people’s preferences and interests
Based on the interest sequences, we introduce methods to calculate the length of longest common sub-IS (LCSIS) and the count of ACSIS, which are extended by taking into account the sequence and the deviations of users’ ratings on the common items
To evaluate the performance of our method and the effectiveness of IS for recommendations, we compare it with the traditional user-based collaborative filtering-based recommendation (CF) recommendation algorithm and a recommendation algorithm based on users’ dynamic information from [8]: 1. User-based CF (UCF) [43]: is a comparative algorithm that uses the rating history of users to calculate the similarities between them and makes automatic predictions based on those similarities and neighbors’ ratings
Summary
The existing recommendation systems have adopted various methods to derive people’s preferences and interests. All such methods can be divided into three categories: content-based recommendation, collaborative filtering-based recommendation (CF) and hybrid recommendation. Among these three approaches, the collaborative filtering approach is one of the most successful. The collaborative filtering approach is one of the most successful It requires only users’ past behavior, such as their item ratings, browsing history and purchased items, without requiring more extensive knowledge. Over the past decade, neighborbased CF and latent factor model-based CF approaches have been proposed, and their effectiveness and efficiency have been verified in recommendation systems.
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