[論文レビュー] The Deconfounded Recommender: A Causal Inference Approach to Recommendation

Zhaoran Wang, Dawen Liang+2 more|arXiv (Cornell University)|Aug 20, 2018

Advanced Causal Inference Techniques参考文献 6被引用数 43

ひとこと要約

tldr: 脱混乱レコメンダーを提案します。露出モデリングを介した Poisson factorization により substitute confounders を推定し、ratings のデバイアを行い、推奨における未観測の混乱因子に対する頑健性を向上させる、2段階の因果的アプローチ。

ABSTRACT

The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user and movie, what would the rating be if we "forced" the user to watch the movie? To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome." The problem is there may be unobserved confounders, variables that affect both which movies the users watch and how they rate them; unobserved confounders impede causal predictions with observational data. To solve this problem, we develop the deconfounded recommender, a way to use classical recommendation models for causal recommendation. Following Wang & Blei [23], the deconfounded recommender involves two probabilistic models. The first models which movies the users watch; it provides a substitute for the unobserved confounders. The second one models how each user rates each movie; it employs the substitute to help account for confounders. This two-stage approach removes bias due to confounding. It improves recommendation and enjoys stable performance against interventions on test sets.

研究の動機と目的

• Motivate recommendation as a causal inference problem with interventions (watching a movie) and outcomes (ratings).
• Develop a two-model framework to address unobserved confounding in observational data.
• Leverage exposure data to infer substitute confounders and adjust the ratings model accordingly.

提案手法

• Model exposure with Poisson factorization to obtain per-user latent factors as substitute confounders.
• Reconstruct an exposure proxy hat{a}_{ui} from the Poisson factorization posterior.
• Fit an outcome model that includes the substitute confounder to predict ratings when a movie is watched (y_{ui}(1)).
• Compute potential ratings for unseen movies using the fitted outcome model to produce causal recommendations.
• Show that this deconfounding approach reduces bias and yields more robust predictions under confounding.

実験結果

リサーチクエスチョン

• RQ1 How can unobserved confounders bias causal interpretation in recommendation systems?
• RQ2 Can a substitute confounder derived from exposure modeling correct ratings models to achieve unbiased causal predictions?
• RQ3 Does the deconfounded recommender improve rating prediction and ranking under confounded observational data?

主な発見

• The deconfounded recommender yields more accurate rating predictions than classical matrix factorization baselines under varying levels of confounding.
• It outperforms IPW-based causal methods on random test sets in both real and simulated data.
• On Movielens datasets, it achieves lower per-item MSE/MAE than baselines, indicating better universal prediction across items.
• The method remains robust to unobserved confounding and improves NDCG/recall metrics in ranking tasks on random test sets.
• In simulations, the approach shows greater resilience to confounding than classical MF methods and IPW variants.]