Selection bias mitigation in recommender system using uninteresting items based on temporal visibility

[1]  Sira Yongchareon,et al.  High-order autoencoder with data augmentation for collaborative filtering , 2021, Knowl. Based Syst..

[2]  Christian Haas,et al.  Fairness metrics and bias mitigation strategies for rating predictions , 2021, Inf. Process. Manag..

[3]  X. Liao,et al.  Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization , 2021, KDD.

[4]  Bipin Indurkhya,et al.  Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems , 2021, Inf. Process. Manag..

[5]  Chirag Shah,et al.  Implicit information need as explicit problems, help, and behavioral signals , 2020, Inf. Process. Manag..

[6]  Feng Li,et al.  Collaborative Filtering Algorithm based on Data Mixing and Filtering , 2019, International Journal of Performability Engineering.

[7]  Sang-Wook Kim,et al.  Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering , 2019, WWW.

[8]  Sangwon Lee,et al.  Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network , 2017, Inf. Process. Manag..

[9]  Shujian Huang,et al.  Deep Matrix Factorization Models for Recommender Systems , 2017, IJCAI.

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

[11]  Martin Ester,et al.  Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.

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

[13]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[14]  Yiqun Liu,et al.  Predicting the popularity of web 2.0 items based on user comments , 2014, SIGIR.

[15]  Harald Steck,et al.  Evaluation of recommendations: rating-prediction and ranking , 2013, RecSys.

[16]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[17]  Martin Szomszor,et al.  Comparison of implicit and explicit feedback from an online music recommendation service , 2010, HetRec '10.

[18]  Harald Steck,et al.  Training and testing of recommender systems on data missing not at random , 2010, KDD.

[19]  Richard S. Zemel,et al.  Collaborative prediction and ranking with non-random missing data , 2009, RecSys '09.

[20]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[21]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

[22]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[23]  Licai Zhu,et al.  Preliminary data-based matrix factorization approach for recommendation , 2021, Inf. Process. Manag..

[24]  Marko Tkalcic,et al.  Investigating the impact of recommender systems on user-based and item-based popularity bias , 2021, Inf. Process. Manag..

[25]  Yen-Liang Chen,et al.  A movie recommendation method based on users' positive and negative profiles , 2021, Inf. Process. Manag..

[26]  Tommaso Di Noia,et al.  Explaining recommender systems fairness and accuracy through the lens of data characteristics , 2021, Inf. Process. Manag..