Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation

The rapid development of Internet services and social platforms encourages users to share their opinions. To help users give valuable comments, content providers expect the recommender system to offer appropriate suggestions, including specific features of the item described in texts and emojis, which are all considered aspects of the user reviews. Hence, the review aspect recommendation task has become significant, where the key lies in handling personal preferences and semantic correlations between suggested items. This article proposes a correlation-aware review aspect recommender (CARAR) system model by constructing self-representation correlations between different views of review aspects, including textual aspects and emojis to make a personalized recommendation. The dependencies between different textual aspects and emojis can be identified and utilized to facilitate the factorization process to learn user and item latent factors. The cross-view correlation mapping between textual aspects and emojis can be built to enhance the recommendation performance. Moreover, the additional information in the real-world environment is also applied to our model to adjust the recommendation results. We constructed experiments on five self-collected and public datasets and compared with six existing models. The results show that our model can outperform the existing models on review aspects recommendation tasks, validating the effectiveness of our approach.

I. Introduction

Recommender systems, which extract user preferences and recommended content characteristics, aim to provide users with content that matches users’ needs. To encourage users to share their opinions, several feedback mechanisms are designed to guide users to share their views more concisely and clearly [1]. The most widely used explicit feedback mechanism in the recommender systems is the binary and numerical ratings and text reviews. The simple rating mechanism offers users a convenient approach to express their preferences, while many details may be left out. In contrast, writing comprehensive and high-quality text reviews is a time-consuming and unpopular work.