Abstract
Rating prediction, whose goal is to predict user preference for unconsumed items, has become one of the core tasks in recommendation systems. Recently, many deep learning-based methods have been applied to the field of recommendation systems and have achieved great performance, especially when user reviews are available. User reviews usually contain rich semantic information and can reflect the preferences of users. However, user reviews are usually sparse. To alleviate this problem, we propose a method called EMRM, which stands for Enhanced Multi-source Review-based Model for rating prediction, to collect multi-source auxiliary reviews for each user. EMRM not only collects multi-source auxiliary reviews from nearest neighbors but also from farthest neighbors who have dissimilar consuming behaviors and historical rating records, so it can improve both the accuracy and diversity of recommendations. Our method extracts useful semantic information from user reviews and multi-source auxiliary reviews by applying Ordered-Neurons Long Short-Term Memory (ON-LSTM). Experimental results demonstrate that EMRM achieves better rating prediction accuracy than other baselines on three real-world datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anelli, V.W., Noia, T.D., Sciascio, E.D., Ragone, A., Trotta, J.: The importance of being dissimilar in recommendation. In: SAC, pp. 816–821 (2019)
Bao, Y., Fang, H., Zhang, J.: TopicMF: simultaneously exploiting ratings and reviews for recommendation. In: AAAI, pp. 2–8 (2014)
Catherine, R., Cohen, W.W.: TransNets: learning to transform for recommendation. In: RecSys, pp. 288–296 (2017)
Chin, J.Y., Zhao, K., Joty, S.R., Cong, G.: ANR: aspect-based neural recommender. In: CIKM, pp. 147–156 (2018)
Kim, D.H., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: RecSys, pp. 233–240 (2016)
Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., Wu, L.: A capsule network for recommendation and explaining what you like and dislike. In: SIGIR, pp. 275–284 (2019)
Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model: mutual learning between ratings and reviews. In: WWW, pp. 773–782 (2018)
McAuley, J.J., Leskovec, J., Jurafsky, D.: Learning attitudes and attributes from multi-aspect reviews. In: ICDM, pp. 1020–1025 (2012)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Shen, Y., Tan, S., Sordoni, A., Courville, A.C.: Ordered neurons: integrating tree structures into recurrent neural networks. In: ICLR (2019)
Tan, Y., Zhang, M., Liu, Y., Ma, S.: Rating-boosted latent topics: understanding users and items with ratings and reviews. In: IJCAI, pp. 2640–2646 (2016)
Wang, H., Wang, N., Yeung, D.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)
Wang, X., Xiao, T., Tang, J., Ouyang, D., Shao, J.: MRMRP: multi-source review-based model for rating prediction. In: DASFAA (2), pp. 20–35 (2020)
Wu, L., Quan, C., Li, C., Ji, D.: PARL: let strangers speak out what you like. In: CIKM, pp. 677–686 (2018)
Zeng, W., Shang, M.S., Zhang, Q.M., Lu, L., Zhou, T.: Can dissimilar users contribute to accuracy and diversity of personalized recommendation? Int. J. Mod. Phys. C 21(10), 1217–1227 (2010)
Zhang, W., Yuan, Q., Han, J., Wang, J.: Collaborative multi-level embedding learning from reviews for rating prediction. In: IJCAI, pp. 2986–2992 (2016)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: WSDM, pp. 425–434 (2017)
Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: AAAI, pp. 5941–5948 (2019)
Acknowledgments
This work was supported by Major Scientific and Technological Special Project of Guizhou Province (No. 20183002) and Sichuan Science and Technology Program (No. 2019YFG0535).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Xiao, T., Shao, J. (2021). EMRM: Enhanced Multi-source Review-Based Model for Rating Prediction. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-82153-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82152-4
Online ISBN: 978-3-030-82153-1
eBook Packages: Computer ScienceComputer Science (R0)