Abstract
In the next generation of recommender systems, multi- criteria recommendation could be regarded as one of the most important branches. Compared with traditional recommender systems with usually one single rating, multi-criteria recommender systems have several ratings from different aspects, and generally describe users’ interests more accurately. However, owing to the cost of ratings, multi-criteria recommender systems meet more severe data sparsity problem than traditional single criteria recommender systems.
In this paper, We design a new approach to compute the similarity between users, which tackles the challenge posed by data sparsity that one cannot obtain the similarity between users with no common rated items. With a new method of data preprocessing, the features of items are combined to eliminate the effect of noise and evaluation scale. We model the aggregation function using support vector regression which is more accurate and robust than linear regression. The experiments demonstrate that our method produces a better performance, while providing more powerful suitability on sparse and noisy datasets.
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Fan, J., Xu, L. (2013). A Robust Multi-criteria Recommendation Approach with Preference-Based Similarity and Support Vector Machine. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_47
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DOI: https://doi.org/10.1007/978-3-642-39068-5_47
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