Recommendation from Raw Data with Adaptive Compound Poisson Factorization
This work addresses the problem of improving recommendation accuracy from raw data for users and platforms, though it is incremental as it builds on prior Poisson Factorization methods.
The paper tackles the challenge of using raw count data (e.g., play counts, clicks) in recommender systems, which are sparse and over-dispersed, by developing an adaptive compound Poisson Factorization framework; experiments on three datasets show it achieves better recommendation scores compared to existing methods.
Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user. Such data are known to be sparse, over-dispersed and bursty, which makes their direct use in recommender systems challenging, often leading to pre-processing steps such as binarization. The aim of this paper is to build recommender systems from these raw data, by means of the recently proposed compound Poisson Factorization (cPF). The paper contributions are three-fold: we present a unified framework for discrete data (dcPF), leading to an adaptive and scalable algorithm; we show that our framework achieves a trade-off between Poisson Factorization (PF) applied to raw and binarized data; we study four specific instances that are relevant to recommendation and exhibit new links with combinatorics. Experiments with three different datasets show that dcPF is able to effectively adjust to over-dispersion, leading to better recommendation scores when compared with PF on either raw or binarized data.