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SDBPR: Social distance-aware Bayesian personalized ranking for recommendation

机译：SDBPR：社会距离感知贝叶斯的个性化排名为推荐

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摘要

Recommendation systems recommend new items to users. Because training data contain only binary forms of implicit feedback in many cases, such as in loT and loV, one-class collaborative filtering, which can be solved by using rating-based methods to estimate the numeric scores of items or ranking-based methods based on the preferences of each user for items, must be addressed. In addition, because of the sparsity of such data, ranking-based methods are often preferred over rating-based methods when only implicit feedback is available. Social information has recently been used to improve the accuracy of rankings. Traditional approaches simply consider the direct friends of users in a social network, but this process fails to consider the propagation of influence along connections in the social network and cannot reveal the complex graph structure of the social network. In this paper, a novel social distance-aware Bayesian personalized ranking model, called SDBPR, is proposed to generate more accurate recommendations. SDBPR uses a random walk to travel the social network and then makes pairwise assumptions about the ranking order based on the distance between users along the random walk. The experimental results on two real datasets show that the proposed approaches significantly outperform the baseline approaches in terms of ranking prediction. (C) 2018 Elsevier B.V. All rights reserved.
机译：推荐系统向用户推荐新项目。由于培训数据在许多情况下仅包含隐式反馈的二进制形式，例如批次和LOV，单级协同过滤，这可以通过使用基于额定值的方法来估计基于项目的数字或基于排名的方法来解决关于每个用户的偏好，必须解决。另外，由于这种数据的稀疏性，基于排名的方法通常优先于基于额定值的方法，当仅有隐式反馈可用时。最近已被用来提高排名准确性的社交信息。传统方法只是考虑在社交网络中的用户直接朋友，但此过程未能考虑影响社交网络中的连接的影响，无法揭示社交网络的复杂图形结构。本文提出了一种名为SDBPR的新社会距离感知贝叶斯个性化排名模型，以产生更准确的建议。 SDBPR使用随机步行来旅行社交网络，然后基于沿着随机步行的用户之间的距离来使对排名顺序进行成对假设。两个真实数据集的实验结果表明，在排名预测方面，该提出的方法显着优于基线方法。 （c）2018年elestvier b.v.保留所有权利。

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