Modeling Dynamic Missingness Of Implicit Feedback For Recommendation

Authors:
Menghan Wang Zhejiang University
Mingming Gong University of Pittsburgh
Xiaolin Zheng Zhejiang University
Kun Zhang CMU

Introduction:

Implicit feedback is widely used in collaborative filtering methods for recommendation.To model and exploit the dynamics of missingness, the authors propose a latent variable named ``emph{user intent}' to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process.

Abstract:

Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are \emph{missing not at random} (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn user's negative preferences. Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be a essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named ``\emph{user intent}'' to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporate it into matrix factorization (MF) for recommendation. We also explore two types of constraints to achieve a more compact and interpretable representation of \emph{user intents}. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.

You may want to know: