News recommendation systems? purpose is to tackle the immense amount of news and offer personalized recommendations to users. A major issue in news recommendation is to capture the precise news representations for the efficacy of recommended items. Commonly, news contents are filled with well-known entities of different types. However, existing recommendation systems overlook exploiting external knowledge about entities and topical relatedness among the news. To cope with the above problem, in this paper, we propose Topic-Enriched Knowledge Graph Recommendation System(TEKGR). Three encoders in TEKGR handle news titles in two perspectives to obtain news representation embedding: (1) to extract meaning of news words without considering latent knowledge features in the news and (2) to extract semantic knowledge of news through topic information and contextual information from a knowledge graph. After obtaining news representation vectors, an attention network compares clicked news to the candidate news in order to get the user's final embedding. Our TEKGR model is superior to existing news recommendation methods by manipulating topical relations among entities and contextual features of entities. Experimental results on two public datasets show that our approach outperforms state-of-the-art deep recommendation approaches.
|Title of host publication||CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2020 Oct 19|
|Event||29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland|
Duration: 2020 Oct 19 → 2020 Oct 23
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||29th ACM International Conference on Information and Knowledge Management, CIKM 2020|
|Period||20/10/19 → 20/10/23|
Bibliographical noteFunding Information:
This work was supported by the Korea Electric Power Corporation (Grant number:R18XA05). Kyong-Ho Lee is the corresponding author.
© 2020 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)