Preference or Intent? Double Disentangled Collaborative Filtering

• URL: http://arxiv.org/abs/2305.11084v1
• Date: Thu, 18 May 2023 16:13:41 GMT
• Title: Preference or Intent? Double Disentangled Collaborative Filtering
• Authors: Chao Wang, Hengshu Zhu, Dazhong Shen, Wei wu, Hui Xiong
• Abstract summary: In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process. We propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations.
• Score: 34.63377358888368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.

Related papers

• ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
  ComPO is a method to personalize preference optimization in language models. We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
  arXiv  Detail & Related papers  (2024-10-21T14:02:40Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering [9.740376003100437]
  We propose a novel recommendation framework designated as Bilateral Intent-guided Graph Collaborative Filtering (BIGCF) Specifically, we take a closer look at user-item interactions from a causal perspective and put forth the concepts of individual intent. To counter the sparsity of implicit feedback, the feature distributions of users and items are encoded via a Gaussian-based graph generation strategy.
  arXiv  Detail & Related papers  (2024-05-15T02:31:26Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• Towards Explainable Collaborative Filtering with Taste Clusters Learning [43.4512681951459]
  Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits. We propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning.
  arXiv  Detail & Related papers  (2023-04-27T03:08:15Z)
• Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals [8.041047797530808]
  We identify three factors contributing to context effects: users' adaptive weights, the inter-item comparison, and display positions. We propose a context-dependent preference model named Pacos as a unified framework for addressing three factors simultaneously. Experimental results show that the proposed method has better performance than prior works in predicting users' choices.
  arXiv  Detail & Related papers  (2023-03-10T01:49:56Z)
• Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation [59.183016033308014]
  In this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. Our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches.
  arXiv  Detail & Related papers  (2021-05-16T08:06:22Z)
• Bootstrapping User and Item Representations for One-Class Collaborative Filtering [24.30834981766022]
  One-class collaborative filtering (OCCF) is to identify user-item pairs that are positively-related but have not been interacted yet. This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling.
  arXiv  Detail & Related papers  (2021-05-13T14:24:13Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)

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.