融合知识图谱和短期偏好的推荐算法

doi:10.3778/j.issn.1673-9418.2008059

摘要/Abstract

摘要：

近年来，将知识图谱作为辅助信息来增强推荐越来越受到研究者的关注。由于知识图谱学习任务的目标是还原知识图谱中三元组的关系，并非是以推荐任务为目标，导致了知识图谱学习任务很难高效地帮助推荐任务提升推荐性能。另外，用户兴趣易被短期的环境和心情所影响。针对以上两点，提出了一种融合了知识图谱信息和短期偏好的推荐模型（MKASR）。首先，通过RippleNet算法提取用户和知识图谱实体的关系对，然后将这些关系对按照知识图谱三元组的形式存储和参与训练；采用基于注意力机制的双向GRU网络从用户近期交互的物品序列中提取用户的短期偏好；其次，采用多任务学习的方法同时训练知识图谱学习模块和推荐模块，并得到用户和物品的特征表示；最后，通过这些特征表示和用户的短期偏好向用户综合推荐。在真实数据集MovieLens-1M和Book-Crossing上进行实验，采用AUC、ACC、Precision和Recall指标进行评估，实验结果表明，提出的模型优于其他的基准模型。

关键词: 推荐系统, 知识图谱, 短期偏好, 偏好传播, 多任务学习

Abstract:

In recent years, attention has been paid to knowledge graph as auxiliary information to enhance recom-mendation increasedly. Since the goal of the knowledge graph learning task is to restore the relationship of the triples in the knowledge graph, rather than to accomplish the recommendation task, it is difficult for the knowledge graph learning task to efficiently help the recommendation task improve the recommendation performance. In addition, user??s interest is easily affected by short-term environment and mood. This paper proposes a recommendation model that is a multi-task feature learning approach for knowledge graph and short-term preferences enhanced recommendation (MKASR) in response to the above two points. Firstly, the RippleNet algorithm is used to extract the relationship pairs between the user and the knowledge graph entity, and then these relationship pairs are stored in the form of knowledge graph triples for participating in training. The bidirectional GRU (gate recurrent unit) network based on the attention mechanism is adopted from the user??s recent interaction sequence of items to extract the user??s short-term preferences. Secondly, this paper uses the multi-task learning method to train the knowledge graph learning module and the recommendation module. And the feature representation between user and item can be obtained. Finally, these feature representations and the short-term preferences of users are taken into account to make comprehensive recommendations to users. The experiments on real MovieLens-1M and Book-Crossing datasets demonstrate that the proposed model has improved performance compared with other recommendation algorithms in AUC, ACC, Precision and Recall evaluation indexes.

Key words: recommender systems, knowledge graph, short-term preferences, preference propagation, multi-task learning

高仰, 刘渊. 融合知识图谱和短期偏好的推荐算法[J]. 计算机科学与探索, 2021, 15(6): 1133-1144.

GAO Yang, LIU Yuan. Recommendation Algorithm Combining Knowledge Graph and Short-Term Preferences[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1133-1144.

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