Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 12, Pages 10762-10774Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3171335
Keywords
Correlation; Recommender systems; Task analysis; Computational modeling; Feature extraction; Writing; Semantics; Emojis; matrix factorization; multilabel learning; recommender system; textual aspects
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This article proposes a correlation-aware review aspect recommender (CARAR) system model that can make personalized recommendations by constructing self-representation correlations between different views of review aspects. The model can identify and utilize dependencies between different aspects and enhance recommendation performance through cross-view correlation mapping. Experimental results demonstrate the effectiveness of the approach in review aspect recommendation tasks.
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.
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