计算机科学
情绪分析
强化学习
推荐系统
知识图
构造(python库)
图形
情报检索
人工智能
机器学习
自然语言处理
理论计算机科学
程序设计语言
作者
Sung-Jun Park,Dong-Kyu Chae,Hong-Kyun Bae,Sumin Park,Sang–Wook Kim
标识
DOI:10.1145/3488560.3498515
摘要
Explainable recommendation has gained great attention in recent years. A lot of work in this research line has chosen to use the knowledge graphs (KG) where relations between entities can serve as explanations. However, existing studies have not considered sentiment on relations in KG, although there can be various types of sentiment on relations worth considering (e.g., a user's satisfaction on an item). In this paper, we propose a novel recommendation framework based on KG integrated with sentiment analysis for more accurate recommendation as well as more convincing explanations. To this end, we first construct a Sentiment-Aware Knowledge Graph (namely, SAKG) by analyzing reviews and ratings on items given by users. Then, we perform item recommendation and reasoning over SAKG through our proposed Sentiment-Aware Policy Learning (namely, SAPL) based on a reinforcement learning strategy. To enhance the explainability for end-users, we further developed an interactive user interface presenting textual explanations as well as a collection of reviews related with the discovered sentiment. Experimental results on three real-world datasets verified clear improvements on both the accuracy of recommendation and the quality of explanations.
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