Dual-embedding based Neural Collaborative Filtering for Recommender Systems

• URL: http://arxiv.org/abs/2102.02549v2
• Date: Fri, 5 Feb 2021 11:12:04 GMT
• Title: Dual-embedding based Neural Collaborative Filtering for Recommender Systems
• Authors: Gongshan He, Dongxing Zhao, Lixin Ding
• Abstract summary: We propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering. In addition to learning the primitive embedding for a user (an item), we introduce an additional embedding from the perspective of the interacted items (users) to augment the user (item) representation.
• Score: 0.7949579654743338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. \textit{embedding}) and then model the interactions between users and items based on the representations. Despite its effectiveness, we argue that it's insufficient to yield satisfactory embeddings for collaborative filtering. Inspired by the idea of SVD++ that represents users based on themselves and their interacted items, we propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering, to utilize historical interactions to enhance the representation. In addition to learning the primitive embedding for a user (an item), we introduce an additional embedding from the perspective of the interacted items (users) to augment the user (item) representation. Extensive experiments on four publicly datasets demonstrated the effectiveness of our proposed DNCF framework by comparing its performance with several traditional matrix factorization models and other state-of-the-art deep learning based recommender models.

Related papers

• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• Cross-Attribute Matrix Factorization Model with Shared User Embedding [0.5266869303483376]
  We introduce a refined NeuMF model that considers not only the interaction between users and items, but also acrossing associated attributes. Our proposed architecture features a shared user embedding, seamlessly integrating with user embeddings to imporve the robustness and effectively address the cold-start problem.
  arXiv  Detail & Related papers  (2023-08-14T17:15:37Z)
• UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering [2.921387082153523]
  We propose UIPC-MF, a prototype-based matrix factorization method for explainable collaborative filtering recommendations. To enhance explainability, UIPC-MF learns connection weights that reflect the associative relations between user and item prototypes for recommendations. UIPC-MF outperforms other prototype-based baseline methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain on three datasets.
  arXiv  Detail & Related papers  (2023-08-14T10:18:24Z)
• 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)
• Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach [56.12815715932561]
  We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
  arXiv  Detail & Related papers  (2022-04-20T01:25:08Z)
• Consistent Collaborative Filtering via Tensor Decomposition [0.2578242050187029]
  Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. We develop Sliced Anti-symmetric Decomposition (SAD), a new model for collaborative filtering based on implicit feedback. We demonstrate the efficiency of SAD in both simulated and real world datasets containing over 1M user-item interactions.
  arXiv  Detail & Related papers  (2022-01-28T05:14:37Z)
• SelfCF: A Simple Framework for Self-supervised Collaborative Filtering [72.68215241599509]
  Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. We propose a self-supervised collaborative filtering framework (SelfCF) that is specially designed for recommender scenario with implicit feedback. We show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.
  arXiv  Detail & Related papers  (2021-07-07T05:21:12Z)
• Sparse-Interest Network for Sequential Recommendation [78.83064567614656]
  We propose a novel textbfSparse textbfInterest textbfNEtwork (SINE) for sequential recommendation. Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool. SINE can achieve substantial improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-18T11:03:48Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)
• 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.