An Interpretable Recommendations Approach Based on User Preferences and Knowledge Graph

Zhong, Yanru; Song, Xiulai; Yang, Bing; Jiang, Chaohao; Luo, Xiangfeng

doi:10.1007/978-3-030-26354-6_33

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Before 2018 [10,62,63] 2018 [2, 11∼13, 22, 52, 64∼67] 2019 [44, 47∼51, 61, 68∼73] Path-based methods Before 2018 [16,21,53] 2018 [18] 2019 [14,19,20,45,74] 申克的救赎》)⇒(用户 A, 喜欢,《肖申克的救赎》)…”

Section: Embedding-based Methodsmentioning

confidence: 99%

“…其中 p e;ej , p e;e 和 p ej ;ej 分别表示 e 与 e j , e 与 e 和 e j 与 e j 之间的路径, MovieLens-1M [10,12,13,18,19,44,46,47,50,63,74,91] Amazon e-commerce [66,67] MovieLens-20M [12,20,20,48 Figure 4 (Color online) An illustration of entity linking for news data 谱 [43] 进行匹配. 在电影数据中, 知识图谱上关于电影的相关补充特征 (例如: 导演、演员和电影类型等) 可以作为辅助信息有效地帮助我们提升推荐系统的效果.…”

Section: 基于知识图谱的推荐算法分类unclassified

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A survey on knowledge graph-based recommender systems

et al. 2020

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Figure 1 (Color online) An illustration of explainable knowledge graph-based recommendation 扮演着至关重要的角色, 用来促进商务业务发展以及辅助决策过程, 并且广泛地应用于各大电子商务 (淘宝、Amazon、eBay 等) 和多媒体 (MovieLens、豆瓣) 等网站中. 个性化推荐算法是推荐系统的核心, 其主要可以被分为 3 类, 即基于内容的推荐算法、基于协同过滤的推荐算法和混合推荐算法 [5]. 其中, 协同过滤方法通过利用用户历史的行为偏好数据构建模型, 取得了巨大的成功 [6∼8]. 这种方法的优势在于不需要类似基于内容的推荐算法那样对物品进行复杂的特征提取与建模. 尽管这类协同过滤方法通常是有效且普适的, 但其依旧存在着多种问题. 主要包括用户和物品之间的行为关系数据的稀疏问题和对新用户或者新物品进行推荐时存在的冷启动问题. 为此, 研究者尝试将协同过滤推荐算法和其他辅助信息相结合 (例如, 用户与物品的属性特征、用户社交网络信息等) 搭建混合推荐系统来解决以上问题, 从而提升推荐效果 [9∼11]. 知识图谱作为混合推荐系统中一类有效的辅助信息, 近年来受到了大量研究者的关注 [2, 10, 12∼15] .

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Section: Embedding-based Methodsmentioning

confidence: 99%

Section: 基于知识图谱的推荐算法分类unclassified

A survey on knowledge graph-based recommender systems

et al. 2020

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“…In recent years, methods based on knowledge graphs have been proposed to interpret recommendations such as KPUP [92] and entity2rec [93], which in addition to presenting a higher accuracy, make recommendations easily interpretable. Although some works have been developed using knowledge graphs, it is necessary to focus on how to use KG technologies to solve interpretability problems and to design interpretable models that lead to the explainability of the recommendation results.…”

Section: Interpretability Of Recommendationsmentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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Knowledge Graph Recommendation Algorithm Combined with PersonalRank Sampling

Jin,

Feng

2024

2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE)

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An Interpretable Recommendations Approach Based on User Preferences and Knowledge Graph

Cited by 3 publications

References 18 publications

A survey on knowledge graph-based recommender systems

A survey on knowledge graph-based recommender systems

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Knowledge Graph Recommendation Algorithm Combined with PersonalRank Sampling

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