Abstract
As one of the most important techniques in recommender systems, collaborative filtering (CF) generates the recommendations or predictions based on the observed preferences. Most traditional recommender systems fail to discover the latent associations between the same or similar items with different names, which is called synonymy problem. With the rapid increasing number of users and items, the user-item rating data is extremely sparse. Based on the limited number of user ratings, we cannot capture enough information from the user history using the traditional CF techniques, which could reduce the effectiveness of the recommender systems.
In this paper, we propose a novel model User-Relation-Item Model (URIM) for CF, which exploits the latent relationship between different user interest domains and item types. By introducing a component named user-item-relation matrix, which reflects the latent major association patterns behind users and items, URIM tackles the synonymy problem, and therefore achieves a significant performance improvement. We compared our method with several state-of-the-art recommendation algorithms on two real-world datasets. Experimental results validate the effectiveness of our model in terms of prediction accuracy (RMSE) and top-N recommendation quality (Recall and Precision). More specifically, URIM reduces the RMSE by nearly 10 % and 5 % on the two datasets, respectively.
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Zhou, Y., Song, B., Zheng, HT. (2015). Exploiting Latent Relations Between Users and Items for Collaborative Filtering. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_41
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DOI: https://doi.org/10.1007/978-3-319-26555-1_41
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