A Model-Agnostic Causal Learning Framework for Recommendation using Search Data

TL;DR

This paper introduces IV4Rec, a model-agnostic causal learning framework that decomposes recommendation embeddings into causal and non-causal parts using search behavior as instrumental variables, improving recommendation accuracy.

Contribution

The paper proposes a novel causal decomposition framework for recommendation systems that leverages search data as instrumental variables, enhancing recommendation performance.

Findings

IV4Rec improves recommendation accuracy across datasets.

The framework effectively separates causal from non-causal information.

It is compatible with multiple existing recommendation models.

Abstract

Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich information for recommendation, such as items, users, and contexts, as embedding vectors and leverage them to predict users' feedback. In the view of causal analysis, the associations between these embedding vectors and users' feedback are a mixture of the causal part that describes why an item is preferred by a user, and the non-causal part that merely reflects the statistical dependencies between users and items, for example, the exposure mechanism, public opinions, display position, etc. However, existing RSs mostly ignored the striking differences between the causal parts and non-causal parts when using these embedding vectors. In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results. Specifically, we jointly consider users' behaviors in search scenarios and recommendation scenarios. Adopting the concepts in causal analysis, we embed users' search behaviors as instrumental variables (IVs), to help decompose original embedding vectors in recommendation, i.e., treatments. IV4Rec then combines the two parts through deep neural networks and uses the combined results for recommendation. IV4Rec is model-agnostic and can be applied to a number of existing RSs such as DIN and NRHUB. Experimental results on both public and proprietary industrial datasets demonstrate that IV4Rec consistently enhances RSs and outperforms a framework that jointly considers search and recommendation.