Logistic Matrix Factorization for Implicit Feedback Data

Abstract

Collaborative filtering with implicit feedback data involves recommender system techniques for analyzing relationships betweens users and items using implicit signals such as click through data or music streaming play counts to provide users with personalized recommendations. This is in contrast to collaborative filtering with explicit feedback data which aims to model these relationships using explicit signals such as user-item ratings. Since most data on the web comes in the form of implicit feedback data there is an increasing demand for collaborative filtering methods that are designed for the implicit case. In this paper we present Logistic Matrix Factorization (Logistic MF), a new probabilistic model for matrix factorization with implicit feedback. The model is simple to implement, highly parallelizable, and has the added benefit that it can model the probability that a user will prefer a specific item. Additionally, we show it to experimentally outperform the widely adopted Implicit Matrix Factorization method using a dataset composed of music listening behavior from streaming music company Spotify

Related

September 2020 | RecSys

Contextual and Sequential User Embeddings for Large-Scale Music Recommendation

Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas

September 2020 | RecSys

Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions

Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas

September 2020 | RecSys

Investigating Listeners’ Responses to Divergent Recommendations

Rishabh Mehrotra, Chirag Shah, Benjamin Carterette