Response Aware Model-Based Collaborative Filtering

Guang Ling, Haiqin Yang, Michael R. Lyu, Irwin King
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:499-508, 2012.

Abstract

Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users’ response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-ling12a, title = {Response Aware Model-Based Collaborative Filtering}, author = {Ling, Guang and Yang, Haiqin and Lyu, Michael R. and King, Irwin}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {499--508}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/ling12a/ling12a.pdf}, url = {https://proceedings.mlr.press/r10/ling12a.html}, abstract = {Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users’ response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Response Aware Model-Based Collaborative Filtering %A Guang Ling %A Haiqin Yang %A Michael R. Lyu %A Irwin King %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-ling12a %I PMLR %P 499--508 %U https://proceedings.mlr.press/r10/ling12a.html %V R10 %X Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users’ response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects. %Z Reissued by PMLR on 04 October 2026.
APA
Ling, G., Yang, H., Lyu, M.R. & King, I.. (2012). Response Aware Model-Based Collaborative Filtering. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:499-508 Available from https://proceedings.mlr.press/r10/ling12a.html. Reissued by PMLR on 04 October 2026.

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