| Summary: | An Explainable Recommendation system has become more prominent because it could convince the user to trust the prediction. However, the existing Explainable Recommendation systems face one of these two limitations. The first limitation is the utilization of a pre-defined feature set to represent the users/items’ preferences. By representing the preferences with a pre-defined feature set, the uniqueness of each user will be limited by the characteristics of the pre-defined set. For the other limitation, the user/item preference representation in existing models is constructed from the entire past interactions. This can mislead the model because the user’s preferences change over the interaction’s characteristics. Therefore, the entire past interactions might not be able to represent the user’s preferences for the target interaction. In this paper, we proposed an Explainable Recommendation system, which represents the user’s preferences with a personalized feature set and is able to adapt the target user/item preference representation to suit the target interaction’s characteristics. By representing the users/items’ preferences with a personalized feature set, the proposed method is able to give the score of importance in the rating contribution to each feature and uses these scores to assume the reasons for the user’s decision. To make the representation suitable to the target interaction’s characteristics, the proposed model utilizes only the target interaction’s information to represent the preferences of users/items. The proposed method also is not relying on the entire past interactions to avoid the bias of dominant features in the past interactions. For the experiment, the proposed method consistently outperforms all baselines on three benchmark datasets. We also analyze the explanation resulting from the proposed model to support our assumptions that the preferences of the user are unique and dynamically change over the interaction’s characteristics.
|
|---|