Abstract
Most of the existing recommender systems understand the preference level of users based on user-item interaction ratings. Rating-based recommendation systems mostly ignore negative users/reviewers (who give poor ratings). There are two types of negative users. Some negative users give negative or poor ratings randomly, and some negative users give ratings according to the quality of items. Some negative users, who give ratings according to the quality of items, are known as reliable negative users, and they are crucial for a better recommendation. Similar characteristics are also applicable to positive users. From a poor reflection of a user to a specific item, the existing recommender systems presume that this item is not in the user’s preferred category. That may not always be correct. We should investigate whether the item is not in the user’s preferred category, whether the user is dissatisfied with the quality of a favorite item or whether the user gives ratings randomly/casually. To overcome this problem, we propose a Social Promoter Score (SPS)-based recommendation. We construct two user-item interaction matrices with users’ explicit SPS value and users’ view activities as implicit feedback. With these matrices as inputs, our attention layer-based deep neural model deepCF_SPS learns a common low-dimensional space to present the features of users and items and understands the way users rate items. Extensive experiments on online review datasets present that our method can be remarkably futuristic compared to some popular baselines. The empirical evidence from the experimental results shows that our model is the best in terms of scalability and runtime over the baselines.
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Moreno MN, Segrera S, López V F, Muñoz M D, Sánchez ÁL (2016) Web mining based framework for solving usual problems in recommender systems. a case study for movies’ recommendation. Neurocomputing 176:72–80
Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370
Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on Recommender systems, ACM, pp 93–100
Ji K, Sun R, Li X, Shu W (2016) Improving matrix approximation for recommendation via a clustering-based reconstructive method. Neurocomputing 173:912–920
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
Mandal S, Maiti A (2018) Explicit feedbacks meet with implicit feedbacks: a combined approach for recommendation system. In: International conference on complex networks and their applications, Springer, pp 169–181
He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (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
Park C, Kim D, Oh J, Yu H (2017) Do also-viewed products help user rating prediction?. In: Proceedings of the 26th international conference on world wide web, international world wide web conferences steering committee, pp 1113–1122
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, ACM, pp 233–240
Chin JY, Zhao K, Joty S, Cong G (2018) Anr: Aspect-based neural recommender. In: Proceedings of the 27th ACM international conference on information and knowledge management, ACM, pp 147–156
Chen C, Zhang M, Liu Y, Ma S (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
Zheng X, He W, Li L (2019) Distributed representations based collaborative filtering with reviews. Appl Intell 49(7):2623–2640
Zhou M, Ding Z, Tang J, Yin D (2018) Micro behaviors: A new perspective in e-commerce recommender systems. In: Proceedings of the eleventh ACM international conference on web search and data mining, ACM, pp 727–735
Bao Y, Fang H, Zhang J (2014) Topicmf: Simultaneously exploiting ratings and reviews for recommendation. In: Twenty-Eighth AAAI conference on artificial intelligence
Polato M, Aiolli F (2018) Boolean kernels for collaborative filtering in top-n item recommendation. Neurocomputing 286:214–225
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, AUAI Press, pp 452–461
Chen S, Peng Y (2018) Matrix factorization for recommendation with explicit and implicit feedback. Knowl-Based Syst 158:109–117
Li HL, Cao J, Jiang H, Alsaedi A (2018) Finite-time synchronization of fractional-order complex networks via hybrid feedback control. Neurocomputing 320:69–75
He R, McAuley J (2016) Vbpr: visual bayesian personalized ranking from implicit feedback. In: Thirtieth AAAI conference on artificial intelligence
Chen C, Zhang M, Zhang Y, Liu Y, Ma S (2020) Efficient neural matrix factorization without sampling for recommendation. ACM Trans Inf Syst (TOIS) 38(2):1–28
Zhang S, Yao L, Xu X (2017) Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 957–960
Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, pp 3203–3209
Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Thirty-first AAAI conference on artificial intelligence
Cheng W, Shen Y, Zhu Y, Huang L (2018) Delf: A dual-embedding based deep latent factor model for recommendation. In: IJCAI, pp 3329–3335
Zhang W, Zhang X, Wang H, Chen D (2019) A deep variational matrix factorization method for recommendation on large scale sparse dataset. Neurocomputing 334:206–218
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
Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web, ACM, pp 111–112
Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining, ACM, pp 153–162
Chen J, Wang X, Zhao S, Qian F, Zhang Y (2020) Deep attention user-based collaborative filtering for recommendation. Neurocomputing 383:57–68
Maiti A, Mandal S (2019) System and method for determining company performance. Publication Date (U/S 11A): 21/06/2019, Application No: 201731045513, Indian Patent (Published)
Mandal S, Maiti A (2020) Explicit feedback meet with implicit feedback in gpmf: a generalized probabilistic matrix factorization model for recommendation. Appl Intell 50(6):1955–1978
Srivastava N, Salakhutdinov RR (2012) Multimodal learning with deep boltzmann machines. In: Advances in neural information processing systems, pp 2222–2230
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:14126980
He R, McAuley J (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, international world wide web conferences steering committee, pp 507–517
McAuley J, Targett C, Shi Q, Van Den Hengel A (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 43–52
He X, Zhang H, Kan MY, Chua TS (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 549–558
Bayer I, He X, Kanagal B, Rendle S (2017) A generic coordinate descent framework for learning from implicit feedback. In: Proceedings of the 26th international conference on world wide web, international world wide web conferences steering committee, pp 1341–1350
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, international world wide web conferences steering committee, pp 278–288
He X, Chen T, Kan MY, Chen X (2015) Trirank: Review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1661–1670
Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, vol 2007, pp 5–8
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Mandal, S., Maiti, A. Deep collaborative filtering with social promoter score-based user-item interaction: a new perspective in recommendation. Appl Intell 51, 7855–7880 (2021). https://doi.org/10.1007/s10489-020-02162-9
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DOI: https://doi.org/10.1007/s10489-020-02162-9