71
Views
3
CrossRef citations to date
0
Altmetric
Articles

S-SNHF: sentiment based social neural hybrid filtering

&
Pages 297-325 | Received 03 Jan 2022, Accepted 26 Feb 2023, Published online: 24 Apr 2023

References

  • Adomavicius, G., and A. Tuzhilin. 2005. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions.” IEEE Transactions on Knowledge and Data Engineering 17 (6): 734–749. doi:10.1109/TKDE.2005.99
  • Al-Ghuribi, S. M., and S. A. Mohd Noah. 2019. “Multi-criteria Review-Based Recommender System: The State of the Art.” IEEE Access 7: 169446–169468. doi:10.1109/ACCESS.2019.2954861
  • Avesani, P., P. Massa, and R. Tiella. 2005. “A Trust-Enhanced Recommender System Application: Moleskiing.” Proceedings of the 2005 ACM Symposium on Applied Computing, 1589–1593. doi:10.1145/1066677.1067036
  • Bathla, G., H. Aggarwal, and R. Rani. 2020. “AutoTrustRec: Recommender System with Social Trust and Deep Learning Using AutoEncoder.” Multimedia Tools and Applications 79: 20845–20860. doi:10.1007/s11042-020-08932-4.
  • Batmaz, Z., A. Yurekli, C. Bilge, and C. Kaleli. 2019. “A Review on Deep Learning for Recommender Systems: Challenges and Remedies.” Artificial Intelligence Review 52: 1–37. doi:10.1007/s10462-018-9654-y
  • Berkani, L., I. Kerboua, and S. Zeghoud. 2020. “Recommandation Hybride basée sur l’Apprentissage Profond.” Actes de la conférence EDA 2020, Revue des Nouvelles Technologies de l'Information, RNTI B.16. ISBN: 979-10-96289-13-4, 69–76.
  • Berkani, L., D. Laga, and A. Aissat. 2021. “Social Neural Hybrid Recommendation with Deep Representation Learning.” Christian Attiogbé and Sadok Ben Yahia (eds) Model and Data Engineering, 10th International Conference MEDI 2021, Tallinn, 127–140.
  • Berkani, L., S. Zeghoud, and I. Kerboua. 2022. “Chapter 19 - Neural Hybrid Recommendation Based on GMF and Hybrid MLP.” In Artificial Intelligence and Machine Learning for EDGE Computing, edited by Rajiv Pandey, Sunil Kumar Khatri, Neeraj kumar Singh, and Parul Verma, 287–303. Academic Press. ISBN 9780128240540. doi:10.1016/B978-0-12-824054-0.00030-7.
  • Bhattacharya, S., D. Sarkar, D.-K. Kole, and P. Jana. 2022. “Chapter 9 - Recent Trends in Recommendation Systems and Sentiment Analysis.” In Hybrid Computational Intelligence for Pattern Analysis, Advanced Data Mining Tools and Methods for Social Computing, edited by Sourav De, Sandip Dey, Siddhartha Bhattacharyya, and Surbhi Bhatia, 163–175. Academic Press. ISBN 9780323857086. doi:10.1016/B978-0-32-385708-6.00016-3.
  • Burke, R. 2002. “Hybrid Recommender Systems: Survey and Experiments.” User Modeling and User Adapted Interaction 12 (4): 331–370. doi:10.1023/A:1021240730564
  • Chen, L., G. Chen, and F. Wang. 2015. “Recommender Systems Based on User Reviews: The State of the art.” User Modeling and User-Adaptive Interaction 25 (2): 99–154. doi:10.1007/s11257-015-9155-5
  • Chen, C., M. Zhang, Y. Liu, and S. Ma. 2018. “Neural Attentional Rating Regression with Review-level Explanations.” WWW ‘18: Proceedings of the 2018 World Wide Web Conference, April 2018, 1583–1592. doi:10.1145/3178876.3186070.
  • Contratres, F. G., S. N. Alves-Souza, L. V. L. Filgueiras, and L. S. Desouza. 2018, March 27–29. “Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems.” In Proceedings of the World Conference on Information Systems and Technologies, Naples, Italy, 122–132. Berlin: Springer.
  • Dang, C. N., M. N. Moreno-García, and F. De la Prieta. 2021. “An Approach to Integrating Sentiment Analysis Into Recommender Systems.” Sensors 21: 5666. doi:10.3390/s21165666.
  • Da Silva, E. D. S., H. Langseth, and H. Ramampiaro. 2017. “Content-Based Social Recommendation with Poisson Matrix Factorization.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 530–546.
  • Deng, S., L. Huang, G. Xu, X. Wu, and Z. Wu. 2017. “On Deep Learning for Trust-Aware Recommendations in Social Networks.” IEEE Transactions on Neural Networks and Learning Systems 28: 1164–1177. doi:10.1109/TNNLS.2016.2514368
  • Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2018. “Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv 2018, arXiv:preprint/04805.
  • Diao, Q., M. Qiu, C. Y. Wu, A. J. Smola, J. Jiang, and C. Wang. 2014. “Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation.” Proceedings of the 20th ACM SIGKDD Intern. Conference on Knowledge Discovery and Data Mining, 193–202.
  • Duantengchuan, L., H. Liu, Z. Zhang, K. Lin, S. Fang, Z. Li, and N.-N. Xiong. 2021. “CARM: Confidence-Aware Recommender Model via Review Representation Learning and Historical Rating Behavior in the Online Platforms.” Neurocomputing 455: 283–296. doi:10.1016/j.neucom.2021.03.122
  • El Yebdri, Z., S. M. Benslimane, F. Lahfa, M. and Barhamgi, and D. Benslimane. 2021. “Context-aware Recommender System Using Trust Network.” Computing 103: 1919–1937. doi:10.1007/s00607-020-00876-9.
  • Guerreiro, J., and P. Rita. 2020. “How to Predict Explicit Recommendations in Online Reviews Using Text Mining and Sentiment Analysis.” Journal of Hospitality and Tourism Management 43: 269–272. doi:10.1016/j.jhtm.2019.07.001
  • Guo, G., J. Zhang, and N. Yorke-Smith. 2015. “Trustsvd: Collaborative filtering with Both the Explicit and Implicit Inuence of User Trust and of Item Ratings”. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 123–129.
  • Harris, D., and S. Harris. 2012. Digital Design and Computer Architecture. 2nd ed. San Francisco, Calif.: Morgan Kaufmann, p. 129. ISBN 978-0-12-394424-5.
  • He, X., X. Du, X. Wang, F. Tian, J. Tang, and T.-S. Chua. 2018. “Outer Product-Based Neural Collaborative Filtering.” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), 2227–2233.
  • He, X., L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua. 2017. “Neural Collaborative Filtering.” Proceedings of the 26th Intern. Conference on World Wide Web, 173–182.
  • Hu, B., C. Shi, W. X. Zhao, and P. S. Yu. 2018. “Leveraging Meta-Path based Context for Top-N recommendation with a Neural Co-Attention Model.” Proceedings of ACM SIGKDD, 1530–1540.
  • Jamali, M., and M. Ester. 2010. “A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks”. RecSys'10 - Proceedings of the 4th ACM Conf. on Recommender Systems. 135–142. doi:10.1145/1864708.1864736.
  • Jiang, L., L. Liu, J. Yao, and L. Shi. 2020. “A Hybrid Recommendation Model in Social Media Based on Deep Emotion Analysis and Multi-Source View Fusion.” Journal of Cloud Computing: Advances, Systems and Applications 9: 57. doi:10.1186/s13677-020-00199-2.
  • Kim, Y. 2014. “Convolutional Neural Networks for Sentence Classification.” Proceedings of the 2014 Conf. on Empirical Methods in Natural Language Processing, Doha, Qatar.
  • Kim, M. W., E. J. Kim, and J. W. Ryu. 2005. “Collaborative Filtering for Recommendation Using Neural Networks.” Proceedings of the fifth international conference on computational science and its applications, ICCSA’05. 127–136.
  • Kim, D., C. Park, J. Oh, S. Lee, and H. Yu. 2016. “Convolutional Matrix Factorization for Document Context-Aware Recommendation.” Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), 233–240.
  • Kleinberg, J. 1999. “Authoritative Sources in a Hyperlinked Environment.” Journal of the ACM 46 (5): 604–632. doi:10.1145/324133.324140.
  • Koren, Y. 2008. “Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model.” Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’08, 426–434. ACM, New York.
  • Li, M., L. Sheng, Y. Song, and J. Song. 2022. “An Enhanced Matrix Completion Method Based on Non-Negative Latent Factors for Recommendation System.” Expert Systems with Applications 201: 116985. ISSN 0957-4174. doi:10.1016/j.eswa.2022.116985.
  • Liu, D., J. Li, B. Du, J. Chang, R. Gao, and Y. Wu. 2021. “A Hybrid Neural Network Approach to Combine Textual Information and Rating Information for Item Recommendation.” Knowledge and Information Systems 63: 621–646. doi:10.1007/s10115-020-01528-2.
  • Liu, H., Y. Wang, Q. Peng, F. Wu, L. Gan, L. Pan, and P. Jiao. 2020. “Hybrid Neural Recommendation with Joint Deep Representation Learning of Ratings and Reviews.” Neuro-Computing 374: 77–85.
  • Lu, Y., R. Dong, and B. Smyth. 2018. “Coevolutionary Recommendation Model: Mutual Learning Between Ratings and Reviews.” Proceedings of the WWW, 773–782.
  • Ma, H., D. Zhou, C. Liu, M. Lyu, and I. King. 2011. “Recommender Systems with Social Regularization.” Proceedings of the fourth ACM international conference on web search and data mining, New York: ACM, 287–296.
  • McAuley, J. J., and J. Leskovec. 2013. “From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise Through Online Reviews.” Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013, 897–908.
  • Mikolov, T., I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. 2013. “Distributed Representations of Words and Phrases and their Compositionality.” Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3111–3119.
  • Nahta, R., Y. K. Meena, D. G. Ganpat, and S. Chauhan. 2021. “Embedding Metadata Using Deep Collaborative filtering to Address the Cold Start Problem for the Rating Prediction Task.” Multimedia Tools and Applications 80: 18553–18581. doi:10.1007/s11042-021-10529-4.
  • Ngaffo, A.-N., and Z. Choukair. 2022. “A Deep Neural Network-Based Collaborative Filtering Using a Matrix Factorization with a Twofold Regularization.” Neural Computing and Applications 34: 6991–7003. doi:10.1007/s00521-021-06831-9.
  • Ni, J., Z. Huang, J. Cheng, and S. Gao. 2021. “An Effective Recommendation Model Based on Deep Representation Learning.” Information Sciences 542 (1): 324–342. doi:10.1016/j.ins.2020.07.038
  • Nisha, C. C., and A. Mohan. 2019. “A Social Recommender System Using Deep Architecture and Network Embedding.” Applied Intelligence 49: 1937–1953. doi:10.1007/s10489-018-1359-z.
  • Osman, N. A., and S. A. Mohd Noah. 2018. “Sentiment-Based Model for Recommender Systems.” Proceedings of the Fourth International Conference on Information Retrieval and Knowledge Management (CAMP).
  • Osman, N. A., S. A. Mohd Noah, and M. Darwich. 2019. “Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain.” International Journal of Machine Learning and Computing 9: 425–431. doi:10.18178/ijmlc.2019.9.4.821
  • Osman, N. A., S. A. Mohd Noah, M. Darwich, and M. Mohd. 2021. “Integrating Contextual Sentiment Analysis in Collaborative Recommender Systems.” PLoS ONE 16 (3): e0248695. doi:10.1371/journal.pone.0248695.
  • Pan, Y., F. He, and H. Yu. 2019. “A Novel Enhanced Collaborative Auto-Encoder with Knowledge Distillation for Top-N Recommender Systems.” Neurocomputing 332: 137–148. doi:10.1016/j.neucom.2018.12.025
  • Pan, Y., F. He, and H. Yu. 2020. “Learning Social Representations with Deep Auto-Encoder for Recommender System.” World Wide Web 23: 2259–2279. doi:10.1007/s11280-020-00793-z.
  • Peis, E., J. M. Morales-del-Castillo, and J. A. Delgado-López. 2008. “Semantic Recommender Systems.” In Analysis of the State of the Topic. Hipertext.net, 6.
  • Rama, K., P. Kumar, and B. Bhasker. 2021. “Deep Autoencoders for Feature Learning with Embeddings for Recommendations: A Novel Recommender System Solution.” Neural Computing and Applications 33: 14167–14177. doi:10.1007/s00521-021-06065-9.
  • Rebentrost, P., A. Steffens, I. Marvian, and S. Lloyd. 2018. “Quantum Singular-Value Decomposition of Nonsparse Low-Rank Matrices.” Physical Review A 97 (1): 012327. doi:10.1103/PhysRevA.97.012327
  • Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. 1994. “GroupLens: An Open Architecture for Collaborative Filtering of Netnews.” Proceedings of the Computer Supported Cooperative Work Conference, 175-186.
  • Salakhutdinov, R., and A. Mnih. 2007. “Probabilistic Matrix Factorization.” In Adv. Neural Information Processing Systems, 20, 1257–1264.
  • Sarwar, B., G. Karypis, J. Konstan, and J. Reidl. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms.” Proceedings of the 10th International Conference on World Wide Web, 285–295.
  • Shokeen, J., and C. Rana. 2020. “A Study on Features of Social Recommender Systems.” Artificial Intelligence Review 53: 965–988. doi:10.1007/s10462-019-09684-w.
  • Sun, Z., L. Han, W. G. Huang, X. Wang, X. Zeng, M. Wang, and H. Yan. 2015. “Recommender Systems Based on Social Networks.” Journal of Systems and Software 99: 109–119. doi:10.1016/j.jss.2014.09.019
  • Wankhade, M., A.-C. Sekhara Rao, and C. Kulkarni. 2022. “A Survey on Sentiment Analysis Methods, Applications, and Challenges.” Artificial Intelligence Review. doi:10.1007/s10462-022-10144-1.
  • Xie, M., H. Yin, H. Wang, F. Xu, W. Chen, and S. Wang. 2016. “Learning Graph-Based POI Embedding for Location-Based Recommendation.” Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM ‘16, 15–24. ACM, New York.
  • Yang, C., X. Chen, L. Liu, and P. Sweetser. 2021. “Leveraging Semantic Features for Recommendation: Sentence-Level Emotion Analysis.” Information Processing and Management 58: 102543. doi:10.1016/j.ipm.2021.102543
  • Yang, B., Y. Lei, J. Liu, and W. and Li. 2017. “Social Collaborative Filtering by Trust.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (8): 1633–1647. doi:10.1109/TPAMI.2016.2605085
  • Yin, H., W. Wang, H. Wang, L. Chen, and X. Zhou. 2017. “Spatial-aware Hierarchical Collaborative Deep Learning for POI Recommendation.” IEEE Transactions on Knowledge and Data Engineering 29: 2537–2551. doi:10.1109/TKDE.2017.2741484
  • Zhang, S., L. Yao, A. Sun, and Y. Tay. 2017. “Deep Learning Based Recommender System: A Survey and New Perspectives.” ArXiv 170707435. doi:10.1145/3285029.
  • Zhao, T., J. McAuley, and I. King. 2014. “Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering.” Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, 261–270.
  • Zheng, L., V. Noroozi, and P. S. Yu. 2017. “Joint Deep Modeling of Users and Items Using Reviews for rec.” arXiv:1701.04783. doi:10.48550/arXiv.1701.04783.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.