Deconfounded Causal Collaborative Filtering

• URL: http://arxiv.org/abs/2110.07122v2
• Date: Mon, 14 Aug 2023 16:04:21 GMT
• Title: Deconfounded Causal Collaborative Filtering
• Authors: Shuyuan Xu and Juntao Tan and Shelby Heinecke and Jia Li and Yongfeng Zhang
• Abstract summary: Recommender systems may be confounded by various types of confounding factors (also called confounders) We propose Deconfounded Causal Collaborative Filtering (DCCF) to solve the problem. Our method is able to deconfound unobserved confounders to achieve better recommendation performance.
• Score: 56.41625001876272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model for each specific confounder. However, real-world systems may include a huge number of confounders and thus designing each specific model for each specific confounder could be unrealistic. More importantly, except for those ``explicit confounders'' that experts can manually identify and process such as item's position in the ranking list, there are also many ``latent confounders'' that are beyond the imagination of experts. For example, users' rating on a song may depend on their current mood or the current weather, and users' preference on ice creams may depend on the air temperature. Such latent confounders may be unobservable in the recorded training data. To solve the problem, we propose Deconfounded Causal Collaborative Filtering (DCCF). We first frame user behaviors with unobserved confounders into a causal graph, and then we design a front-door adjustment model carefully fused with machine learning to deconfound the influence of unobserved confounders. Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.

Related papers

• Causal Structure Representation Learning of Confounders in Latent Space for Recommendation [6.839357057621987]
  Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. We consider the influence of confounders, disentangle them from user preferences in the latent space, and employ causal graphs to model their interdependencies.
  arXiv  Detail & Related papers  (2023-11-02T08:46:07Z)
• Separating and Learning Latent Confounders to Enhancing User Preferences Modeling [6.0853798070913845]
  We propose a novel framework, Separating and Learning Latent Confounders For Recommendation (SLFR) SLFR obtains the representation of unmeasured confounders to identify the counterfactual feedback by disentangling user preferences and unmeasured confounders. Experiments in five real-world datasets validate the advantages of our method.
  arXiv  Detail & Related papers  (2023-11-02T08:42:50Z)
• Debiasing Recommendation by Learning Identifiable Latent Confounders [49.16119112336605]
  Confounding bias arises due to the presence of unmeasured variables that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. We propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables to resolve the aforementioned non-identification issue.
  arXiv  Detail & Related papers  (2023-02-10T05:10:26Z)
• Causal Disentanglement with Network Information for Debiased Recommendations [34.698181166037564]
  Recent research proposes to debias by modeling a recommender system from a causal perspective. The critical challenge in this setting is accounting for the hidden confounders. We propose to leverage network information (i.e., user-social and user-item networks) to better approximate hidden confounders.
  arXiv  Detail & Related papers  (2022-04-14T20:55:11Z)
• Deep Causal Reasoning for Recommendations [47.83224399498504]
  A new trend in recommender system research is to negate the influence of confounders from a causal perspective. We model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. We show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space.
  arXiv  Detail & Related papers  (2022-01-06T15:00:01Z)
• Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
  We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions. We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
  arXiv  Detail & Related papers  (2021-11-26T10:57:47Z)
• Top-N Recommendation with Counterfactual User Preference Simulation [26.597102553608348]
  Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. In this paper, we propose to reformulate the recommendation task within the causal inference framework to handle the data scarce problem.
  arXiv  Detail & Related papers  (2021-09-02T14:28:46Z)
• Federated Mixture of Experts [94.25278695272874]
  FedMix is a framework that allows us to train an ensemble of specialized models. We show that users with similar data characteristics select the same members and therefore share statistical strength.
  arXiv  Detail & Related papers  (2021-07-14T14:15:24Z)
• Causal Collaborative Filtering [50.22155187512759]
  Causal Collaborative Filtering is a framework for modeling causality in collaborative filtering and recommendation. We show that many traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We propose a conditional intervention approach for $do$-operations so that we can estimate the user-item causal preference.
  arXiv  Detail & Related papers  (2021-02-03T04:16:11Z)

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.