NDLI: Bayesian binomial mixture model for collaborative prediction with non-random missing data

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Bayesian binomial mixture model for collaborative prediction with non-random missing data

Content Provider	ACM Digital Library
Author	Kim, Yong-Deok Choi, Seungjin
Abstract	Collaborative prediction involves filling in missing entries of a user-item matrix to predict preferences of users based on their observed preferences. Most of existing models assume that the data is missing at random (MAR), which is often violated in recommender systems in practice. Incorrect assumption on missing data ignores the missing data mechanism, leading to biased inferences and prediction. In this paper we present a Bayesian binomial mixture model for collaborative prediction, where the generative process for data and missing data mechanism are jointly modeled to handle non-random missing data. Missing data mechanism is modeled by three factors, each of which is related to users, items, and rating values. Each factor is modeled by Bernoulli random variable, and the observation of rating value is determined by the Boolean OR operation of three binary variables. We develop computationally-efficient variational inference algorithms, where variational parameters have closed-form update rules and the computational complexity depends on the number of observed ratings, instead of the size of the rating data matrix. We also discuss implementation issues on hyperparameter tuning and estimation based on empirical Bayes. Experiments on Yahoo! Music and MovieLens datasets confirm the useful behavior of our model by demonstrating that: (1) it outperforms state-of-the-art methods in yielding higher predictive performance; (2) it finds meaningful solutions instead of undesirable boundary solutions; (3) it provides rating trend analysis on why ratings are observed.
Starting Page	201
Ending Page	208
Page Count	8
File Format	PDF MP4
ISBN	9781450326681
DOI	10.1145/2645710.2645754
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Non-random missing data Collaborative filtering Probabilistic models Variational bayesian inference Recommender systems
Content Type	Audio Text
Resource Type	Article

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