Abstract
Recommender systems play an increasingly important role in online applications characterized by a very large amount of data and help users to find what they need or prefer. Various approaches for recommender systems have been developed that utilize either demographic, content, or historical information. Among these methods, item-based collaborative filtering is one of most widely used and successful neighborhood-based collaborative recommendation approaches that compute recommendation for users using the similarity between different items. However, despite their success, they suffer from the lack of available ratings which leads to poor recommendations. In this paper we apply a bi-criterion bath optimization approach on a graph representing the items and their similarity. This approach introduces additional similarity links by combining two or more existing links and improve the similarity matrix between items. The two criteria take into account on the one hand the distance between items on a the graph (min sum criterion), on the other hand the estimate of the information reliability (max min criterion). Experimental results on both explicit and implicit datasets shows that our approach is able to burst the accuracy of existing item-based algorithms and to outperform other algorithms.
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Rostami, B., Cremonesi, P., Malucelli, F. (2013). A Graph Optimization Approach to Item-Based Collaborative Filtering. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 470. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00410-5_2
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DOI: https://doi.org/10.1007/978-3-319-00410-5_2
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