Optimizing Social-Topic Engagement on Social Network and Knowledge Graph

Teng, Ya-Wen; Shi, Yishuo; Tsai, Jui-Yi; Shuai, Hong-Han; Tai, Chih-Hua; Yang, De-Nian

doi:10.1109/globecom38437.2019.9013546

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…As shown in Table 2, most of the analyzed works on KG employ a hybrid approach combining more than one recommendation method. Apart from these methods, there are other recommendation approaches that can also be combined with knowledge graphs, e.g., user group recommendation approaches [9,83], conversational approaches [84,85], and social network-based [86,87].…”

Section: Recommendation and Artificial Intelligence Methodsmentioning

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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