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Leveraging the Dynamic Changes from Items to Improve Recommendation

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Conceptual Modeling (ER 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11157))

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Abstract

User-generated reviews contain rich information, which has been ignored by most of recommender systems. Recently, some recommender systems using reviews with deep learning techniques have demonstrated that they can potentially alleviate the sparsity problem and improve the quality of recommendation. However, they only consider the dynamic interests from users but ignoring the changed properties of items. In this paper, we present a deep model which can capture not only the common users behaviors, the changed users interests and fundamental item properties, but also the changed properties of items. Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms all baseline recommender systems.

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Notes

  1. 1.

    https://code.google.com/p/word2vec.

  2. 2.

    http://www.yelp.com.

  3. 3.

    http://www.airlinequality.com.

  4. 4.

    https://github.com/quankiquanki/skytrax-reviews-dataset.

  5. 5.

    http://www.trip.com.

  6. 6.

    http://keras.io.

References

  1. Baccianella, S., Esuli, A., Sebastiani, F.: Multi-facet rating of product reviews. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 461–472. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00958-7_41

    Chapter  Google Scholar 

  2. Bao, Y., Fang, H., Zhang, J.: TopicMF: simultaneously exploiting ratings and reviews for recommendation. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 2–8 (2014)

    Google Scholar 

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  4. Blunsom, P., Grefenstette, E., Kalchbrenner, N.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (2014)

    Google Scholar 

  5. Bo, P., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Meeting on Association for Computational Linguistics, pp. 115–124 (2005)

    Google Scholar 

  6. Chen, Y.: Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo (2015)

    Google Scholar 

  7. Collobert, R.: Natural language processing from scratch. J. Mach. Learn. Res. 12, 2393–2537 (2011)

    MATH  Google Scholar 

  8. Dai, H., Wang, Y., Trivedi, R., Song, L.: Deep coevolutionary network: embedding user and item features for recommendation. In: KDD (2017)

    Google Scholar 

  9. Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: International Conference on Neural Information Processing Systems, pp. 2643–2651 (2013)

    Google Scholar 

  10. Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2

    Book  MATH  Google Scholar 

  11. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. Computer Science (2015)

    Google Scholar 

  12. Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: ACM International Conference on Conference on Information and Knowledge Management, pp. 2333–2338 (2013)

    Google Scholar 

  13. Koren and Yehuda: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2009)

    Article  Google Scholar 

  14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  15. Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3168–3177 (2014)

    Article  Google Scholar 

  16. Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 811–820. ACM (2015)

    Google Scholar 

  17. Ling, G., Lyu, M.R., King, I.: Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 105–112. ACM (2014)

    Google Scholar 

  18. Mcauley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: ACM Conference on Recommender Systems, pp. 165–172 (2013)

    Google Scholar 

  19. Mitchell, J., Lapata, M.: Composition in distributional models of semantics. Cogn. Sci. 34(8), 1388 (2010)

    Article  Google Scholar 

  20. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: International Conference on International Conference on Machine Learning, pp. 807–814 (2010)

    Google Scholar 

  21. Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798. ACM (2007)

    Google Scholar 

  22. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

  23. Song, Y., Elkahky, A.M., He, X.: Multi-rate deep learning for temporal recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 909–912 (2016)

    Google Scholar 

  24. Tang, D., Qin, B., Liu, T., Yang, Y.: User modeling with neural network for review rating prediction. In: International Conference on Artificial Intelligence, pp. 1340–1346 (2015)

    Google Scholar 

  25. Tang, D., Wei, F., Qin, B., Zhou, M., Liu, T.: Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 172–182 (2014)

    Google Scholar 

  26. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Meeting of the Association for Computational Linguistics, pp. 1555–1565 (2014)

    Google Scholar 

  27. Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)

    Google Scholar 

  28. Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792 (2010)

    Google Scholar 

  29. Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 153–162. ACM (2016)

    Google Scholar 

  30. Wu, Y., Ester, M.: Flame: a probabilistic model combining aspect based opinion mining and collaborative filtering. In: Eighth ACM International Conference on Web Search and Data Mining, pp. 199–208 (2015)

    Google Scholar 

  31. Yichao, W., Liu, Y.: Robust truncated hinge loss support vector machines. Publ. Am. Stat. Assoc. 102(479), 974–983 (2007)

    Article  MathSciNet  Google Scholar 

  32. Xiong, L., Chen, X., Huang, T.K., Schneider, J., Carbonell, J.G.: Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: SIAM International Conference on Data Mining, SDM 2010, 29 April–1 May 2010, Columbus, Ohio, USA, pp. 211–222 (2010)

    Google Scholar 

  33. Yu, H.F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization, Computer Science (2015)

    Google Scholar 

  34. Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372 (2013)

    Google Scholar 

  35. Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Tenth ACM International Conference on Web Search and Data Mining, pp. 425–434 (2017)

    Google Scholar 

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Acknowledgment

This work was supported by National Key Research and Development Plan (2016QY02D0402).

Author information

Authors and Affiliations

  1. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Zongze Jin, Yun Zhang & Weimin Mu

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Zongze Jin, Yun Zhang, Weimin Mu, Weiping Wang & Hai Jin

  3. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Hai Jin

  4. National Engineering Research Center Information Security Common Technology, NERCIS, Beijing, China

    Weiping Wang

Authors
  1. Zongze Jin
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  2. Yun Zhang
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  3. Weimin Mu
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  4. Weiping Wang
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  5. Hai Jin
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Corresponding author

Correspondence to Yun Zhang .

Editor information

Editors and Affiliations

  1. Lucentia, University of Alicante, Alicante, Spain

    Juan C. Trujillo

  2. Miami University, Oxford, OH, USA

    Karen C. Davis

  3. Renmin University of China, Beijing, China

    Xiaoyong Du

  4. Northwestern Polytechnical University, Xian, China

    Zhanhuai Li

  5. Department of Computer Science, National University of Singapore, Singapore, Singapore

    Tok Wang Ling

  6. Department of Computer Science and Technology, Tsinghua University, Beijing, Beijing, China

    Guoliang Li

  7. National University of Singapore, Singapore, Singapore

    Mong Li Lee

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Jin, Z., Zhang, Y., Mu, W., Wang, W., Jin, H. (2018). Leveraging the Dynamic Changes from Items to Improve Recommendation. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_37

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  • DOI: https://doi.org/10.1007/978-3-030-00847-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00846-8

  • Online ISBN: 978-3-030-00847-5

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