UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
By Haekyu Park, Hyunsik Jeon, Junghwan Kim, Beunguk Ahn, U. Kang. 2017
37 citations recorded by the source corpus.
How can we leverage social network data and observed ratings to correctly recommend proper items and provide a persuasive explanation for the recommendations? Many online services provide social networks among users, and it is crucial to utilize social information since recommendation by a friend is more likely to grab attention than the one from a random user. Also, explaining why items are recommended is very important in encouraging the users' actions such as actual purchases. Exploiting both ratings and social graph for recommendation, however, is not trivial because of the heterogeneity of the data. In this paper, we propose UniWalk, an explainable and accurate recommender system that exploits both social network and rating data. UniWalk combines both data into a unified graph, learns latent features of users and items, and recommends items to each user through the features. Importantly, it explains why items are recommended together with the recommendation results. Extensive experiments show that UniWalk provides the best explainability and achieves the state-of-the-art-accuracy.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Information Systems
Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “12: Explanations based on similar users, where the similar users can be social friends or users that have the same preference on the same subset of products (Park et al. [2017]).”