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| 结合注意力机制的知识感知推荐算法 | |
| 引用本文： | 张昕,刘思远,徐雁翎. 结合注意力机制的知识感知推荐算法[J]. 计算机工程与应用, 2022, 58(9): 168-174. DOI: 10.3778/j.issn.1002-8331.2011-0054 |
| 作者姓名： | 张昕 刘思远 徐雁翎 |
| 作者单位： | 辽宁大学 信息学院，沈阳 110036 |
| 基金项目： | 辽宁省公共舆情与网络安全大数据系统工程实验室资助项目;辽宁省博士科研启动基金;国家自然科学基金 |
| 摘 要： | 知识图谱在推荐系统中的应用越来越受重视,可以有效地解决推荐系统中存在的数据稀疏性和冷启动问题.但现有的基于路径和基于嵌入的知识感知推荐算法在合并知识图谱中的实体来表示用户时,并没有考虑到实体对于用户的重要性并不相同,推荐结果会受到无关实体的影响.针对现有方法的局限性,提出了一种新的结合注意力机制的知识感知推荐算法,并给... |
| 关 键 词： | 推荐系统 知识图谱 注意力机制 实体传播 |
Knowledge-Aware Recommendation Algorithm Combined with Attention Mechanism |
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| ZHANG Xin,LIU Siyuan,XU Yanling. Knowledge-Aware Recommendation Algorithm Combined with Attention Mechanism[J]. Computer Engineering and Applications, 2022, 58(9): 168-174. DOI: 10.3778/j.issn.1002-8331.2011-0054 | |
| Authors: | ZHANG Xin LIU Siyuan XU Yanling |
| Affiliation: | College of Information, Liaoning University, Shenyang 110036, China |
| Abstract: | The application of knowledge graphs in recommender systems has attracted more and more attention, which can effectively solve the data sparsity and cold start problems in recommender systems. However, when the existing path-based and embedded-based knowledge-aware recommendation algorithms merge entities in the knowledge graph to represent users, they do not consider that the importance of entities to users is not the same, and the recommendation results will be affected by unrelated entities. Aiming at the limitations of the existing methods, a new knowledge-aware recommendation algorithm combined with the attention mechanism is proposed, and an end-to-end framework for incorporating the knowledge graph into the recommendation system is given. From the user’s historical click items, multiple entity sets are expanded on the knowledge graph, and the user’s preference distribution is calculated through the attention mechanism, and the final click probability is predicted accordingly. Through comparative experiments with traditional recommendation algorithms on two real public data sets, the results show that this method has achieved significant improvement under the evaluation of multiple common indicators（such as AUC, ACC and Recall@top-K）. |
| Keywords: | recommendation system knowledge graph attention mechanism entity communication |
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