Collaborative Filtering and the Missing at Random Assumption

Abstract

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of user-selected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings.

Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)

References in corpus (1)

• Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Cited by in corpus (27)

• Advances and Challenges in Conversational Recommender Systems: A Survey
• Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth
• KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
• Recommendations as Treatments: Debiasing Learning and Evaluation
• ESCM${}^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
• Statistical Significance of the Netflix Challenge
• Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach
• Unbiased Knowledge Distillation for Recommendation
• Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
• Deconfounded Causal Collaborative Filtering
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems
• Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data
• Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
• not-MIWAE: Deep Generative Modelling with Missing not at Random Data
• Pareto Invariant Representation Learning for Multimedia Recommendation
• CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things
• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Fast Matrix Factorization with Non-Uniform Weights on Missing Data
• Modeling User Exposure in Recommendation
• Stochastic Gradient Descent for Linear Systems with Missing Data
• Flatter is better: Percentile Transformations for Recommender Systems
• Choice Set Confounding in Discrete Choice
• Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation
• Deep Latent Emotion Network for Multi-Task Learning
• Meta-learning for Matrix Factorization without Shared Rows or Columns
• Learning from missing data with the Latent Block Model
• A Multiresolution Analysis Framework for the Statistical Analysis of Incomplete Rankings