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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

14.08.2020

Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering

verfasst von: Xiaoxin Sun, Haobo Zhang, Meiqi Wang, Mengying Yu, Minghao Yin, Bangzuo Zhang

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

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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.
Vorheriger Artikel Bipartite Synchronization Analysis of Fractional Order Coupled Neural Networks with Hybrid Control
Nächster Artikel An Application of Generalized Fuzzy Hyperbolic Model for Solving Fractional Optimal Control Problems with Caputo–Fabrizio Derivative

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Literatur
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.
20.
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):668–681CrossRef
Metadaten
Titel
Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering
verfasst von
Xiaoxin Sun
Haobo Zhang
Meiqi Wang
Mengying Yu
Minghao Yin
Bangzuo Zhang
Publikationsdatum
14.08.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10333-5

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