Knowledge discovery from numerical data

Morita, Chie; Tsukimoto, Hiroshi

doi:10.1016/s0950-7051(98)00040-9

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…The Kullback-Leibler (KL) distance [22] is commonly used to measure the distance of probability distributions in probability theory, so we use it to compute the user similarity in this paper. For user u and v, the KL distance between u P and v P is computed as follows:…”

Section: Definition 3 (Top-k Permutation Set) Given An Item Setmentioning

confidence: 99%

Recommendations Based on Listwise Learning-to-Rank by Incorporating Social Information

2018

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Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two

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Section: Definition 3 (Top-k Permutation Set) Given An Item Setmentioning

confidence: 99%