  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.
