Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations

• URL: http://arxiv.org/abs/2401.06633v2
• Date: Wed, 31 Jan 2024 11:07:32 GMT
• Title: Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
• Authors: Lei Li, Jianxun Lian, Xiao Zhou, Xing Xie
• Abstract summary: 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.
• Score: 50.03560306423678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval models aim at selecting a small set of item candidates which match the preference of a given user. They play a vital role in large-scale recommender systems since subsequent models such as rankers highly depend on the quality of item candidates. However, most existing retrieval models employ a single-round inference paradigm, which may not adequately capture the dynamic nature of user preferences and stuck in one area in the item space. In this paper, we propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems that iteratively refines user representations to better capture potential candidates in the full item space. Ada-Retrieval comprises two key modules: the item representation adapter and the user representation adapter, designed to inject context information into items' and users' representations. The framework maintains a model-agnostic design, allowing seamless integration with various backbone models such as RNNs or Transformers. We perform experiments on three widely used public datasets, incorporating five powerful sequential recommenders as backbone models. Our results demonstrate that Ada-Retrieval significantly enhances the performance of various base models, with consistent improvements observed across different datasets. Our code and data are publicly available at: https://github.com/ll0ruc/Ada-Retrieval.

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)
• 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)
• Recommender Systems with Generative Retrieval [58.454606442670034]
  We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
  arXiv  Detail & Related papers  (2023-05-08T21:48:17Z)
• Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model [18.109035867113217]
  Multi-Interest Preference (MIP) model is an approach that produces multi-interest for users by using the user's sequential engagement more effectively. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-14T04:29:54Z)
• kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval [7.681386867564213]
  kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets.
  arXiv  Detail & Related papers  (2022-05-12T16:42:24Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  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.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)
• MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection [110.87712780017819]
  We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. We conduct experiments on two public datasets and a real-world production dataset.
  arXiv  Detail & Related papers  (2020-01-22T16:05:01Z)

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.