{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:15:05Z","timestamp":1770524105731,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202200649"],"award-info":[{"award-number":["KJQN202200649"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Review-aware recommendation systems aim to enhance recommendation performance by leveraging user reviews and their associated attributes to model user preferences. However, most existing methods fail to address two critical challenges introduced by user reviews: polarity bias and temporal dynamics. Polarity bias refers to inconsistencies between a user\u2019s numerical ratings and the sentiment expressed in their reviews\u2014for example, a user might give a restaurant a high rating while writing a negative review. In addition, user preferences may evolve over time, as individuals can review the same item on multiple occasions. To address these issues, we propose RARPT, a review-aware recommendation framework that jointly models polarity and temporality. Specifically, we process positive and negative reviews separately and employ a sequential model to capture the temporal evolution of user preferences. We also introduce a polarity balance module, which uses a cross-attention mechanism to generate supplementary collaborative vectors from reviews of the opposite polarity, thereby mitigating both quantitative and relational imbalances. We conduct extensive experiments on two real-world datasets from Amazon and Yelp. The results show that our proposed model significantly outperforms several state-of-the-art baselines. Moreover, our model offers enhanced interpretability, helping deliver more effective personalized recommendations.<\/jats:p>","DOI":"10.3390\/a18120756","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Review-Aware Recommendation Based on Polarity and Temporality"],"prefix":"10.3390","volume":"18","author":[{"given":"Ye","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xifan","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Visual Computing, School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yulu","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Visual Computing, School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yuhao","family":"Ren","sequence":"additional","affiliation":[{"name":"Global Development Institute, University of Manchester, Manchester M13 9PL, UK"}]},{"given":"Qiao","family":"Zou","sequence":"additional","affiliation":[{"name":"Chongqing Talent Development Center, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1116-4526","authenticated-orcid":false,"given":"Jiacheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Global Development Institute, University of Manchester, Manchester M13 9PL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, K., Song, H., and Suh, B. (2024, January 11). Self-Referential Review: Exploring the Impact of Self-Reference Effect in Review. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201924, New York, NY, USA.","DOI":"10.1145\/3626772.3657969"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Baltrunas, L., Ludwig, B., and Ricci, F. (2011, January 23\u201327). Matrix Factorization Techniques for Context Aware Recommendation. Proceedings of the fifth ACM Conference on Recommender Systems, Chicago, IL, USA.","DOI":"10.1145\/2043932.2043988"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fan, X., Liu, Z., Lian, J., Zhao, W.X., Xie, X., and Wen, J.-R. (2021, January 11\u201315). Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201921, Montreal, QC, Canada.","DOI":"10.1145\/3404835.3462978"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jakob, N., Weber, S.H., M\u00fcller, M.C., and Gurevych, I. (2009, January 6). Beyond the Stars: Exploiting Free-Text User Reviews to Improve the Accuracy of Movie Recommendations. Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, Hong Kong, China. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/1651461.1651473.","DOI":"10.1145\/1651461.1651473"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Safavi, S., Jalali, M., and Houshmand, M. (2022). Toward point-of-interest recommendation systems: A critical review on deep-learning approaches. Electronics, 11.","DOI":"10.3390\/electronics11131998"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2680","DOI":"10.1080\/01621459.2023.2271605","article-title":"Factor augmented sparse throughput deep relu neural networks for high dimensional regression","volume":"119","author":"Fan","year":"2023","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, D., Park, C., Oh, J., Lee, S., and Yu, H. (2016, January 15\u201319). Convolutional Matrix Factorization for Document Context-Aware Recommendation. Proceedings of the 10th ACM Conference on Recommender Systems, RecSys \u201916, Boston, MA, USA.","DOI":"10.1145\/2959100.2959165"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., and Yu, P.S. (2017, January 6\u201310). Joint Deep Modeling of Users and Items Using Reviews for Recommendation. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, UK.","DOI":"10.1145\/3018661.3018665"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Catherine, R., and Cohen, W. (2017, January 27\u201331). Transnets: Learning to Transform for Recommendation. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3109859.3109878.","DOI":"10.1145\/3109859.3109878"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, P., Wang, Z., Ren, Z., Bing, L., and Lam, W. (2017, January 7\u201311). Neural Rating Regression with Abstractive Tips Generation for Recommendation. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201917, Tokyo, Japan.","DOI":"10.1145\/3077136.3080822"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Seo, S., Huang, J., Yang, H., and Liu, Y. (2017, January 27\u201331). Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys \u201917, Como, Italy.","DOI":"10.1145\/3109859.3109890"},{"key":"ref_12","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 1\u201311. Available online: https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html."},{"key":"ref_13","unstructured":"Kenton, J.D.M.W.C., and Toutanova, L.K. (2019, January 2\u20137). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the naacL-HLT, Minneapolis, MN, USA. Available online: https:\/\/au1206.github.io\/assets\/pdfs\/BERT.pdf."},{"key":"ref_14","first-page":"99","article-title":"Deep Learning Recommendation Algorithm Based on Reviews and Item Description s","volume":"49","author":"Wang","year":"2022","journal-title":"Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tay, Y., Luu, A.T., and Hui, S.C. (2018, January 19\u201323). Multi-Pointer Co-Attention Networks for Recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201918, London, UK.","DOI":"10.1145\/3219819.3220086"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.ins.2023.01.051","article-title":"Hybrid recommendation by incorporating the sentiment of product reviews","volume":"625","author":"Elahi","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119911","DOI":"10.1016\/j.eswa.2023.119911","article-title":"Moco4srec: A momentum contrastive learning framework for sequential recommendation","volume":"223","author":"Wei","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., Huang, C., Luo, D., and Lin, K. (May, January 30). Debiased Contrastive Learning for Sequential Recommendation. Proceedings of the ACM Web Conference 2023, WWW \u201923, Austin, TX, USA.","DOI":"10.1145\/3543507.3583361"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Y., and McAuley, J. (2020, January 3\u20137). Time Interval Aware Self-Attention for Sequential Recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, TX, USA.","DOI":"10.1145\/3336191.3371786"},{"key":"ref_20","first-page":"1","article-title":"A review on outlier\/anomaly detection in time series data","volume":"54","author":"Conde","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Srifi, M., Oussous, A., Lahcen, A.A., and Mouline, S. (2020). Recommender systems based on collaborative filtering using review texts\u2014A survey. Information, 11.","DOI":"10.3390\/info11060317"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100423","DOI":"10.1016\/j.cosrev.2021.100423","article-title":"A survey on deep matrix factorizations","volume":"42","author":"Gillis","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shuai, J., Wu, L., Zhang, K., Sun, P., Hong, R., and Wang, M. (2023, January 23\u201327). Topic-Enhanced Graph Neural Networks for Extraction-Based Explainable Recommendation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201923, Taipei, Taiwan.","DOI":"10.1145\/3539618.3591776"},{"key":"ref_24","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"125533","DOI":"10.1016\/j.eswa.2024.125533","article-title":"Integrated sentiment analysis with BERT for enhanced hybrid recommendation systems","volume":"261","author":"Darraz","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ling, G., Lyu, M.R., and King, I. (2014, January 6\u201310). Ratings Meet Reviews, a Combined Approach to Recommend. Proceedings of the 8th ACM Conference on Recommender Systems, RecSys\u201914, Foster City, CA, USA.","DOI":"10.1145\/2645710.2645728"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3295499","article-title":"Spatiotemporal representation learning for translation-based poi recommendation","volume":"37","author":"Qian","year":"2019","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13816","DOI":"10.1609\/aaai.v37i11.26618","article-title":"Factual and Informative Review Generation for Explainable Recommendation","volume":"Volume 37","author":"Xie","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, M., Liu, Y., and Ma, S. (2018, January 23\u201327). Neural Attentional Rating Regression with Review-Level Explanations. Proceedings of the 2018 World Wide Web Conference on World Wide Web WWW \u201918, Lyon, France.","DOI":"10.1145\/3178876.3186070"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, A., Chen, Y., Sheng, L., Wang, X., and Chua, T.S. (2024, January 14\u201318). On Generative Agents in Recommendation. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, USA. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3626772.3657844.","DOI":"10.1145\/3626772.3657844"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Ge, S., Qi, T., Huang, Y., and Xie, X. (2019, January 3\u20137). Neural News Recommendation with Multi-Head Self-Attention. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1671"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Manotumruksa, J., Macdonald, C., and Ounis, I. (2018, January 8\u201312). A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR \u201918, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3210042"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1080\/07421222.2019.1628936","article-title":"Factors affecting the adoption of an electronic word of mouth message: A meta-analysis","volume":"36","author":"Montazemi","year":"2019","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9623","DOI":"10.1609\/aaai.v36i9.21196","article-title":"Do Feature Attribution Methods Correctly Attribute Features?","volume":"Volume 36","author":"Zhou","year":"2022","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, T., Yin, H., Ye, G., Huang, Z., Wang, Y., and Wang, M. (2020, January 25\u201330). Try This Instead: Personalized and Interpretable Substitute Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi\u2019an, China. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3397271.3401042.","DOI":"10.1145\/3397271.3401042"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Ounis, I., and Macdonald, C. (2019). Comparison of Sentiment Analysis and User Ratings in Venue Recommendation, Springer International Publishing.","DOI":"10.1007\/978-3-030-15712-8_14"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sachdeva, N., and McAuley, J. (2020, January 25\u201330). How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201920, Xi\u2019an, China.","DOI":"10.1145\/3397271.3401281"},{"key":"ref_38","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Sardinia, Italy. Available online: https:\/\/proceedings.mlr.press\/v9\/glorot10a."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., and Wu, L. (2019, January 21\u201325). A Capsule Network for Recommendation and Explaining What You Like and Dislike. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201919, Paris, France.","DOI":"10.1145\/3331184.3331216"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4320","DOI":"10.1609\/aaai.v35i5.16557","article-title":"U-BERT: Pre-Training User Representations for Improved Recommendation","volume":"Volume 35","author":"Qiu","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:58:39Z","timestamp":1764961119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,28]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["a18120756"],"URL":"https:\/\/doi.org\/10.3390\/a18120756","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,28]]}}}