Controllable Multi-Interest Framework for Recommendation

• URL: http://arxiv.org/abs/2005.09347v2
• Date: Mon, 3 Aug 2020 02:16:38 GMT
• Title: Controllable Multi-Interest Framework for Recommendation
• Authors: Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, Jie Tang
• Abstract summary: We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
• Score: 64.30030600415654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items that the user might be interacted with. Recent works usually give an overall embedding from a user's behavior sequence. However, a unified user embedding cannot reflect the user's multiple interests during a period. In this paper, we propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity. We conduct experiments for the sequential recommendation on two real-world datasets, Amazon and Taobao. Experimental results demonstrate that our framework achieves significant improvements over state-of-the-art models. Our framework has also been successfully deployed on the offline Alibaba distributed cloud platform.

Related papers

• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  MMGRec aims to introduce a generative paradigm into multimodal recommendation. We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information. We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations [50.03560306423678]
  We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems. Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
  arXiv  Detail & Related papers  (2024-01-12T15:26:40Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)
• A Sequence-Aware Recommendation Method Based on Complex Networks [1.385805101975528]
  We build a network model from data and then use it to predict the user's subsequent actions. The proposed method is implemented and tested experimentally on a large dataset.
  arXiv  Detail & Related papers  (2022-09-30T16:34:39Z)
• Improving Sequential Query Recommendation with Immediate User Feedback [6.925738064847176]
  We propose an algorithm for next query recommendation in interactive data exploration settings. We conduct a large-scale experimental study using log files from a popular online literature discovery service.
  arXiv  Detail & Related papers  (2022-05-12T18:19:24Z)
• GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation [5.416421678129053]
  This paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)" The performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-16T09:12:33Z)
• 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)

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.