Selection bias mitigation in recommender system using uninteresting items based on temporal visibility
暂无分享,去创建一个
Zhan Bu | Shuqing Li | Xiaowei Ding | Lei Shi
[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..