Intent-aware Recommendation Based on Principal Component Analysis

In recommender systems, exploring user intents allows for a better understanding and exploration of user preferences, thereby improving recommendation performance. However, existing methods for modeling user intents often do so by statically setting the intent count, which can result in redundancy or insufficiency in capturing the full range of user intents. In order to solve this problem, this paper proposes a model named I ntent-aware Recommendation Based on P rincipal C omponent A nalysis (Intent PCA). Intent PCA is a novel application of PCA in the field of recommender systems. This model defines intents as users’ preferences for some specific relations shown on a knowledge graph, and constructs a user-relation matrix to calculate users’ preferences for relations. Then PCA is applied on the user-relation matrix to extract user intents. Benefit from good characteristics of PCA, our PCA-based user intent extraction model can comprehensively model user intents while simultaneously avoid intent redundancy. Moreover, by combining the user intents, this paper designs an intent-based information propagation method to differentially aggregate information from surrounding neighbor nodes. Experiments conducted on three datasets validate the effectiveness of the proposed Intent PCA model.