RACMF: robust attention convolutional matrix factorization for rating prediction
Matrix factorization is widely used in collaborative filtering, especially when the data are extremely large and sparse. To deal with the scale and sparsity problem of data, several recommender models adopt users and items’ side information to improve the recommendation results. However, some existi...
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| Veröffentlicht in: | Pattern analysis and applications : PAA Jg. 22; H. 4; S. 1655 - 1666 |
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01.11.2019
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| Abstract | Matrix factorization is widely used in collaborative filtering, especially when the data are extremely large and sparse. To deal with the scale and sparsity problem of data, several recommender models adopt users and items’ side information to improve the recommendation results. However, some existing works do not perform well enough for they are not effectively use the side information. For example, using bag-of-words model, topic model to gain the latent representation of words or merely utilizing items or users’ side information, leads to the result that the performance deteriorates, especially when rating dataset is extremely large and sparse. To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF) model, which is composed of attention convolutional neural network (ACNN) and additional stacked denoising autoencoder (aSDAE); ACNN and aSDAE are used to extract the items’ and users’ latent factors, respectively. The experimental results show that our RACMF model has good prediction ability, even when the rating data are sparse or the scale of rating data is large. What’s more, compared with the state-of-the-art model PHD, the present model RACMF increased the accuracy rate on ML-100k, ML-1m, ML-10m and AIV-6 datasets by 4.80%, 0.57%, 1.98% and 3.67%, respectively. |
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| AbstractList | Matrix factorization is widely used in collaborative filtering, especially when the data are extremely large and sparse. To deal with the scale and sparsity problem of data, several recommender models adopt users and items’ side information to improve the recommendation results. However, some existing works do not perform well enough for they are not effectively use the side information. For example, using bag-of-words model, topic model to gain the latent representation of words or merely utilizing items or users’ side information, leads to the result that the performance deteriorates, especially when rating dataset is extremely large and sparse. To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF) model, which is composed of attention convolutional neural network (ACNN) and additional stacked denoising autoencoder (aSDAE); ACNN and aSDAE are used to extract the items’ and users’ latent factors, respectively. The experimental results show that our RACMF model has good prediction ability, even when the rating data are sparse or the scale of rating data is large. What’s more, compared with the state-of-the-art model PHD, the present model RACMF increased the accuracy rate on ML-100k, ML-1m, ML-10m and AIV-6 datasets by 4.80%, 0.57%, 1.98% and 3.67%, respectively. |
| Author | Shang, Qi Han, Xuli Zeng, Biqing Zeng, Feng Zhang, Min |
| Author_xml | – sequence: 1 givenname: Biqing orcidid: 0000-0001-9088-4759 surname: Zeng fullname: Zeng, Biqing email: zengbiqing0528@163.com organization: School of Computer, South China Normal University – sequence: 2 givenname: Qi surname: Shang fullname: Shang, Qi organization: School of Computer, South China Normal University – sequence: 3 givenname: Xuli surname: Han fullname: Han, Xuli organization: School of Computer, South China Normal University – sequence: 4 givenname: Feng surname: Zeng fullname: Zeng, Feng organization: School of Computer, South China Normal University – sequence: 5 givenname: Min surname: Zhang fullname: Zhang, Min organization: School of Computer, South China Normal University |
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| Cites_doi | 10.1145/2507157.2507163 10.18653/v1/D16-1058 10.1145/3178876.3186070 10.1145/2806416.2806527 10.1145/2645710.2645728 10.1609/aaai.v31i1.10747 10.1109/5.726791 10.1145/2959100.2959165 10.24963/ijcai.2017/447 10.1162/tacl_a_00097 10.1137/1.9781611972764.58 10.1145/3109859.3109890 10.1109/TKDE.2005.99 10.1016/j.ins.2017.06.026 10.1145/2020408.2020480 10.1109/MC.2009.263 10.1145/2939672.2939673 10.1145/2783258.2783273 10.18653/v1/D15-1166 10.1145/3018661.3018665 10.21236/ADA439541 |
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| References_xml | – reference: Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 – reference: 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. ACM, pp 448–456 – reference: Zhang S, Wang W, Ford J, et al (2006) Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, pp 549–553 – reference: 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) – reference: 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 – reference: 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. ACM, pp 165–172 – reference: Liu J, Wang D, Ding Y (2017) PHD: a probabilistic model of hybrid deep collaborative filtering for recommender systems. In: Asian Conference on machine learning, pp 224–239 – reference: AdomaviciusGTuzhilinAToward the next generation of recommender systems: a survey of the state-of-the-art and possible extensionsIEEE Trans Knowl Data Eng200517673474910.1109/TKDE.2005.99 – reference: 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. ACM, pp 105–112 – reference: Seo S, Huang J, Yang H, et al (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. ACM, pp 297–305 – reference: Zhang F, Yuan NJ, Lian D, et al (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 353–362 – reference: Chen C, Zhang M, Liu Y, et al (2018) Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 World Wide Web conference on World Wide Web. International World Wide Web conferences Steering Committee, pp 1583–1592 – reference: Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Advances in neural information processing systems, pp 2643–2651 – reference: 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. ACM, pp 425–434 – reference: KorenYBellRVolinskyCMatrix factorization techniques for recommender systemsComputer20098303710.1109/MC.2009.263 – reference: Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264 – reference: VincentPLarochelleHLajoieIStacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterionJ Mach Learn Res201011Dec3371340827561881242.68256 – reference: Li S, Kawale J, Fu Y (2015) Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 811–820 – reference: Goodfellow I, Pouget-Abadie J, Mirza M, et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680 – reference: Dong X, Yu L, Wu Z, et al (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: AAAI, pp 1309–1315 – reference: YinWSchützeHXiangBABCNN: attention-based convolutional neural network for modeling sentence PairsTrans Assoc Comput Linguist2016425927210.1162/tacl_a_00097 – reference: Wang Y, Huang M, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in Natural Language Processing, pp 606–615 – reference: 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. ACM, pp 1235–1244 – reference: Xu K, Ba J, Kiros R, et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057 – reference: LeCunYBottouLBengioYGradient-based learning applied to document recognitionProc IEEE199886112278232410.1109/5.726791 – reference: Sarwar B, Karypis G, Konstan J, et al (2000) Application of dimensionality reduction in recommender system-a case study. Minnesota Univ Minneapolis Dept of Computer Science – reference: KimDParkCOhJDeep hybrid recommender systems via exploiting document context and statistics of itemsInf Sci2017417728710.1016/j.ins.2017.06.026 – reference: Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 – reference: Xue H, Dai X, Zhang J, et al (2017) Deep matrix factorization models for recommender systems. 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| Title | RACMF: robust attention convolutional matrix factorization for rating prediction |
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