Skip to main content
SpringerLink
Log in

Exploiting review embedding and user attention for item recommendation

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

As a valuable source of user preferences and item properties, reviews have been widely leveraged in many approaches to enhance the performance of recommender systems. Although encouraging success has been obtained, there are two more weaknesses need to be addressed. (1) Most approaches represent users or items merely based on the modeling of review texts, but ignore the potential and latent preferences beyond textual information. (2) Existing methods tend to blindly merge all the previous reviews for user profiling. However, it may be less effective because different interacted items may play distinct roles. Hence, indiscriminately aligning interacted items may limit the model flexibility and performance. In this paper, with the desire to fill these gaps, we design a novel attentive deep review-based recommendation method. In specific, we complement the item representation by an auxiliary vector, based on which a user is then attentively profiled by her posted items to predict the likeness for the target item. Extensive experiments on five real-world datasets demonstrate that our model can not only significantly outperform the state-of-the-art methods, but also provide intuitive explanations to the recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Almahairi A, Kastner K, Cho K, Courville A (2015) Learning distributed representations from reviews for collaborative filtering. In: Proceedings of the 9th ACM conference on recommender systems (RecSys), pp 147–154

  2. Anderson P, He X, Buehler C, Teney D, Johnson M, Gould S, Zhang L (2018) Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 3, p 6

  3. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. In: International conference on learning representations (ICLR)

  4. Bao Y, Fang H, Zhang J (2014) Topicmf: simultaneously exploiting ratings and reviews for recommendation. In: Proceedings of the 28th AAAI conference on artificial intelligence (AAAI), vol 14, pp 2–8

  5. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    MATH  Google Scholar 

  6. Catherine R, Cohen W (2017) Transnets: Learning to transform for recommendation. In: Proceedings of the eleventh ACM conference on recommender systems (RecSys), pp 288–296

  7. Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 335–344

  8. Chen X, Zhang Y, Ai Q, Xu H, Yan J, Qin Z (2017) Personalized key frame recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (SIGIR). ACM, pp 315–324

  9. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)

  10. Cui L, Huang W, Yan Q, Yu FR, Wen Z, Lu N (2018) A novel context-aware recommendation algorithm with two-level svd in social networks. Future Gener Comput Syst 86:1459–1470

    Article  Google Scholar 

  11. Cui L, Ou P, Fu X, Wen Z, Lu N (2017) A novel multi-objective evolutionary algorithm for recommendation systems. J Parallel Distrib Comput 103:53–63

    Article  Google Scholar 

  12. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  13. Guo G, Zhang J, Thalmann D (2012) A simple but effective method to incorporate trusted neighbors in recommender systems. In: International conference on user modeling, adaptation, and personalization (UMAP). Springer, pp 114–125

  14. Guo G, Zhang J, Yorke-Smith N (2015) Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Twenty-ninth AAAI conference on artificial intelligence (AAAI)

  15. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web (WWW), pp 173–182

  16. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  17. Jin M, Luo X, Zhu H, Zhuo HH (2018) Combining deep learning and topic modeling for review understanding in context-aware recommendation. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 1605–1614

  18. Kabbur S, Ning X, Karypis G (2013) Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 659–667

  19. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (ACL), pp 655–665

  20. Kazemi V, Elqursh A (2017) Show, ask, attend, and answer: a strong baseline for visual question answering. arXiv:1704.03162

  21. Kim D, Park C, Oh J, Lee S, Yu, H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems (RecSys), pp 233–240

  22. Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:1408.5882

  23. Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations (ICLR), pp 1–15

  24. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 426–434

  25. Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM conference on recommender systems (RecSys), pp 105–112

  26. Lu J, Yang J, Batra D, Parikh D (2016) Hierarchical question-image co-attention for visual question answering. In: Advances in neural information processing systems (NIPS), pp 289–297

  27. McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems (RecSys), pp 165–172

  28. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML), pp 807–814

  29. Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  30. Rendle S (2010) Factorization machines. In: Proceedings of the 10th IEEE international conference on data mining (ICDM), pp 995–1000

  31. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (UAI), pp 452–461

  32. Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the eleventh ACM conference on recommender systems (RecSys), pp 297–305

  33. Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining (WSDM), pp 565–573

  34. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3156–3164

  35. Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 448–456

  36. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1235–1244

  37. Yang B, Lei Y, Liu D, Liu J (2013) Social collaborative filtering by trust. In: Twenty-third international joint conference on artificial intelligence (IJCAI)

  38. Yang Z, He X, Gao J, Deng L, Smola A (2016) Stacked attention networks for image question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 21–29

  39. Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth ACM international conference on web search and data mining (WSDM), pp 425–434

Download references

Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities under Grant No. N181705007, and by the National Natural Science Foundation of China under Grant Nos. 61972078, 61702084 and 61702090.

Author information

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, China

    Yatong Sun, Guibing Guo & Xingwei Wang

  2. School of Software, Tsinghua University, Beijing, China

    Xu Chen

  3. School of Resource and Civil Engineering, Northeastern University, Shenyang, China

    Penghai Zhang

Authors
  1. Yatong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guibing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penghai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guibing Guo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Guo, G., Chen, X. et al. Exploiting review embedding and user attention for item recommendation. Knowl Inf Syst 62, 3015–3038 (2020). https://doi.org/10.1007/s10115-020-01447-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-020-01447-2

Keywords

Search

Navigation