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Towards Context-Aware Social Recommendation via Trust Networks

机译：通过信任网络了解背景感知社会建议

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Utilizing social network information to improve recommendation quality has recently attracted much attention. However, most existing social recommendation models cannot well handle the heterogeneity and diversity of the social relationships (e.g., different friends may have different recommendations on the same items in different situations). Furthermore, few models take into account (non-social) contextual information, which has been proved to be another valuable information source for accurate recommendation. In this paper, we propose to construct trust networks on top of a social network to measure the quality of a friend's recommendations in different contexts. We employ random walk to collect the most relevant ratings based on the multi-dimensional trustworthiness of users in the trust network. Factorization machines model is then applied on the collected ratings to predict missing ratings considering various contexts. Evaluation based on a real dataset demonstrates that our approach improves the accuracy of the state-of-the-art social, context-aware and trust-aware recommendation models by at least 5.54% and 9.15% in terms of MAE and RMSE respectively.
机译：利用社会网络信息改善建议质量最近引起了很多关注。然而，大多数现有的社会推荐模型不能很好地处理社会关系的异质性和多样性（例如，不同的朋友可能在不同情况下的相同项目上有不同的建议）。此外，很少有模型考虑（非社交）语境信息，已被证明是准确推荐的另一个有价值的信息来源。在本文中，我们建议在社交网络之上构建信任网络，以衡量朋友在不同环境中的建议的质量。我们采用随机步行，以基于信任网络中用户的多维可靠性来收集最相关的评级。然后，在收集的额定值上应用分解机模型以预测考虑各种情况的缺失的评级。基于真实数据集的评估表明，我们的方法分别提高了最先进的社会，背景知识和信任意识推荐模型的准确性，分别在MAE和RMSE方面至少为5.54％和9.15％。

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