A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
Zihua Si,
Xueran Han,
Xiao Zhang
et al.
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 pa… Show more
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