Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

• URL: http://arxiv.org/abs/2202.02576v1
• Date: Sat, 5 Feb 2022 15:17:03 GMT
• Title: Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
• Authors: Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu
• Abstract summary: We propose an unbiased and semantics-aware disentanglement learning called CaDSI. CaDSI explicitly models the causal relations underlying recommendation task. It produces semantics-aware representations via disentangling users true intents aware of specific item context.
• Score: 30.85573846018658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model.

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