Abstract: Recommender systems are important and valuable tools for many personalized
services. Collaborative Filtering (CF) algorithms -- among others -- are
fundamental algorithms driving the underlying mechanism of personalized
recommendation. Many of the traditional CF algorithms are designed based on the
fundamental idea of mining or learning correlative patterns from data for
matching, including memory-based methods such as user/item-based CF as well as
learning-based methods such as matrix factorization and deep learning models.
However, advancing from correlative learning to causal learning is an important
problem, because causal/counterfactual modeling can help us to think outside of
the observational data for user modeling and personalization. In this paper, we
propose Causal Collaborative Filtering (CCF) -- a general framework for
modeling causality in collaborative filtering and recommendation. We first
provide a unified causal view of CF and mathematically show that many of the
traditional CF algorithms are actually special cases of CCF under simplified
causal graphs. We then propose a conditional intervention approach for
$do$-calculus so that we can estimate the causal relations based on
observational data. Finally, we further propose a general counterfactual
constrained learning framework for estimating the user-item preferences.
Experiments are conducted on two types of real-world datasets -- traditional
and randomized trial data -- and results show that our framework can improve
the recommendation performance of many CF algorithms.