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.

Related papers

• Say My Name: a Model's Bias Discovery Framework [18.887645415907166]
  We introduce Say My Name'' (SaMyNa), the first tool to identify biases within deep models semantically. Unlike existing methods, our approach focuses on biases learned by the model. Our method can disentangle task-related information and proposes itself as a tool to analyze biases.
  arXiv  Detail & Related papers  (2024-08-18T18:50:59Z)
• Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement [3.0820287240219795]
  We propose a novel approach to mitigate biases in computer vision models by utilizing counterfactual generation and fine-tuning. Our approach leverages a curriculum learning framework combined with a fine-grained adversarial loss to fine-tune the model using adversarial examples. We validate our approach through both qualitative and quantitative assessments, demonstrating improved bias mitigation and accuracy compared to existing methods.
  arXiv  Detail & Related papers  (2024-04-18T00:41:32Z)
• Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback [55.78118035358662]
  Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values. We have identified that the reward model often finds shortcuts to bypass its intended objectives. We propose an innovative solution, applying the Product-of-Experts technique to separate reward modeling from the influence of sequence length.
  arXiv  Detail & Related papers  (2023-10-08T15:14:39Z)
• Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations [118.0818807474809]
  Abductive reasoning aims to find plausible explanations for an event. Existing approaches for abductive reasoning in natural language processing often rely on manually generated annotations for supervision. This work proposes an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context.
  arXiv  Detail & Related papers  (2023-05-24T01:35:10Z)
• Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation [50.93536377097659]
  This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors.
  arXiv  Detail & Related papers  (2023-04-17T00:10:56Z)
• Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization [77.8735802150511]
  We propose a cross-sample adversarial debiasing (CSAD) method to remove the bias information misused by the target task. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-11T21:17:02Z)
• Generalized Zero-shot Intent Detection via Commonsense Knowledge [5.398580049917152]
  We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents.
  arXiv  Detail & Related papers  (2021-02-04T23:36:41Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings [47.721931801603105]
  We propose OSCaR, a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale. Our experiments on gender biases show that OSCaR is a well-balanced approach that ensures that semantic information is retained in the embeddings and bias is also effectively mitigated.
  arXiv  Detail & Related papers  (2020-06-30T18:18:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.