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
- MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation [63.27390451208503]
Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance.
We propose the Multi-view Disentangled and Adaptive Preference Learning framework.
Our framework uses a multiview encoder to capture diverse user preferences.
arXiv Detail & Related papers (2024-10-08T10:06:45Z) - 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.