Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2019
Abstract
Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system. In this paper, we jointly learn the model of recommendation and knowledge graph completion. Distinct from previous KG-based recommendation methods, we transfer the relation information in KG, so as to understand the reasons that a user likes an item. As an example, if a user has watched several movies directed by (relation) the same person (entity), we can infer that the director relation plays a critical role when the user makes the decision, thus help to understand the user's preference at a finer granularity. Technically, we contribute a new translation-based recommendation model, which specially accounts for various preferences in translating a user to an item, and then jointly train it with a KG completion model by combining several transfer schemes. Extensive experiments on two benchmark datasets show that our method outperforms state-of-the-art KG-based recommendation methods. Further analysis verifies the positive effect of joint training on both tasks of recommendation and KG completion, and the advantage of our model in understanding user preference. We publish our project at https://github.com/TaoMiner/joint-kg-recommender.
Keywords
Item Recommendation, Knowledge Graph, Embedding, Joint Model
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings of the World Wide Web Conference WWW 2019, San Francisco, May 13-17
First Page
151
Last Page
161
ISBN
9781450366748
Identifier
10.1145/3308558.3313705
Publisher
Association for Computing Machinery
City or Country
San Francisco, CA, USA
Citation
CAO, Yixin; WANG, Xiang; HE, Xiangnan; HU, Zikun; and CHUA, Tat-Seng.
Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. (2019). Proceedings of the World Wide Web Conference WWW 2019, San Francisco, May 13-17. 151-161.
Available at: https://ink.library.smu.edu.sg/sis_research/7288
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3308558.3313705
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons