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Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering

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Abstract

Fusing auxiliary information into ratings has shown promising performance for many recommendation tasks, such as age, sex, vocation of users or actors, director, genre, reviews of movies. However, all above auxiliary information is still sparse and not informative. For movie recommendations, besides the above information, there exists richer information in plot texts, exerting huge impacts on improving the recommendation accuracy. In this paper, we explore effective fusion of movie ratings and plot texts, we propose a deep plot-aware generalized matrix factorization for collaborative filtering model, which effectively combines both ratings and plot texts to implement a generalized collaborative filtering. To verify our proposal, we conduct extensive experiments on two popular datasets, and the results perform better than other state-of-the-art approaches in common recommendation tasks.

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References

  1. He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 173–182

  2. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 355–364

  3. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning. ACM, pp 791–798

  4. Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web, pp 278–288

  5. Cheng HT, Koc L, Harmsen J et al (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. ACM, pp 7–10

  6. Shan Y, Hoens TR, Jiao J et al (2016) Deep crossing: web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 255–262

  7. Wang R, Fu B, Fu G et al (2017) Deep and cross network for ad click predictions. In: Proceedings of the ADKDD’17. ACM, p 12

  8. Guo H, Tang R, Ye Y et al (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247

  9. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 191–198

  10. Gong Y, Zhang Q (2016) Hashtag recommendation using attention-based convolutional neural network. In: IJCAI, pp 2782–2788

  11. Seo S, Huang J, Yang H et al (2017) Representation learning of users and items for review rating prediction using attention-based convolutional neural network. In: 3rd International workshop on machine learning methods for recommender systems (MLRec) (SDM’17)

  12. Wang X, Wang Y (2014) Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 627–636

  13. Okura S, Tagami Y, Ono S et al (2017) Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1933–1942

  14. Strub F, Mary J (2015) Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: Proceedings of the NIPS workshop on machine learning for eCommerce

  15. Wu Y, Du Bois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the 9th ACM international conference on websearch and data mining, pp 153–162

  16. Tragas E, Luo C, Gazeau M et al (2018) Scalable recommender systems through recursive evidence chains. arXiv preprint arXiv:1807.02150

  17. Tay Y, Luu AT, Hui SC (2018) Multi-pointer co-attention networks for recommendation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining. ACM

  18. Zhang S, Yao L, Sun A et al (2018) Neurec: on nonlinear transformation for personalized ranking. arXiv preprint arXiv: 1805.03002

  19. He X, Du X, Wang X et al (2018) Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912

  20. Wang H, Zhang F, Xie X et al (2018) DKN: deep knowledge-aware network for news recommendation. arXiv preprint arXiv:1801.08284

  21. Lu Y, Dong R, Smyth B (2018) Coevolutionary recommendation model: mutual learning between ratings and reviews. In: Proceedings of the 2018 world wide web conference on world wide web. International World Wide Web Conferences Steering Committee, pp 773–782

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

  23. Sedhain S, Menon AK, Sanner S, Xie L (2015) AutoRec: autoencodersmeet collaborative filtering. In: Proceedings of the 24th international conference on world wide web, pp 111–112

  24. Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 11–16

  25. Yue L, Sun XX, Gao WZ et al (2018) Multiple auxiliary information based deep model for collaborative filtering. J Comput Sci Technol 33(4):668–681

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Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 71473035, 11501095).

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Authors and Affiliations

  1. Northeast Normal University, Changchun, China

    Xiaoxin Sun, Haobo Zhang, Meiqi Wang, Mengying Yu, Minghao Yin & Bangzuo Zhang

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  1. Xiaoxin Sun
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  2. Haobo Zhang
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  3. Meiqi Wang
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  4. Mengying Yu
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  5. Minghao Yin
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  6. Bangzuo Zhang
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Correspondence to Xiaoxin Sun.

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Sun, X., Zhang, H., Wang, M. et al. Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering. Neural Process Lett 52, 1983–1995 (2020). https://doi.org/10.1007/s11063-020-10333-5

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