Implicit Feedback Recommender System Based on Matrix Factorization

With the development of the internet age, information overload problem is imminent. At now, almost of recommended models use the explicit feedback. But lots of implicit feedback data are missing. The paper explores the area of recommendation based on large-scale implicit feedback, Where only positive feedback is available. Further, the paper carried on the empirical research on the Implicit Feedback Recommendation Model. By maximized the probability of the user's choices, IFR mean the progress task into optimization problems In the way, the experiment results confirm the superiority of the model. However, the model is insufficient about online research and a lack of details.

[1]  Victor C. M. Leung,et al.  CAP: community activity prediction based on big data analysis , 2014, IEEE Network.

[2]  Li Shang,et al.  An algorithm for efficient privacy-preserving item-based collaborative filtering , 2016, Future Gener. Comput. Syst..

[3]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[4]  Min Chen,et al.  iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization , 2017, Future Gener. Comput. Syst..

[5]  Yin Zhang,et al.  GroRec: A Group-Centric Intelligent Recommender System Integrating Social, Mobile and Big Data Technologies , 2016, IEEE Transactions on Services Computing.

[6]  Yin Zhang,et al.  TempoRec: Temporal-Topic Based Recommender for Social Network Services , 2017, Mob. Networks Appl..

[7]  Ole Winther,et al.  Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search , 2016, AAAI.

[8]  M. Shamim Hossain,et al.  TOLA: Topic-oriented learning assistance based on cyber-physical system and big data , 2017, Future Gener. Comput. Syst..

[9]  Julian J. McAuley,et al.  VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.

[10]  Kunle Olukotun,et al.  Understanding and optimizing asynchronous low-precision stochastic gradient descent , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[11]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[12]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[13]  Lixiang Duan,et al.  A new support vector data description method for machinery fault diagnosis with unbalanced datasets , 2016, Expert Syst. Appl..

[14]  Limei Peng,et al.  CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies , 2014, Mobile Networks and Applications.

[15]  Jiming Liu,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Social Collaborative Filtering by Trust , 2022 .

[16]  Sheetal Rathi,et al.  An efficient system using item & user-based CF techniques to improve recommendation , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).