Retrieval Augmentation via User Interest Clustering

• URL: http://arxiv.org/abs/2408.03886v1
• Date: Wed, 07 Aug 2024 16:35:10 GMT
• Title: Retrieval Augmentation via User Interest Clustering
• Authors: Hanjia Lyu, Hanqing Zeng, Yinglong Xia, Ren Chen, Jiebo Luo,
• Abstract summary: Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
• Score: 57.63883506013693
• License:
• Abstract: Many existing industrial recommender systems are sensitive to the patterns of user-item engagement. Light users, who interact less frequently, correspond to a data sparsity problem, making it difficult for the system to accurately learn and represent their preferences. On the other hand, heavy users with rich interaction history often demonstrate a variety of niche interests that are hard to be precisely captured under the standard "user-item" similarity measurement. Moreover, implementing these systems in an industrial environment necessitates that they are resource-efficient and scalable to process web-scale data under strict latency constraints. In this paper, we address these challenges by introducing an intermediate "interest" layer between users and items. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference by clustering engagement graphs and incorporating user-interest attention. This method enhances the understanding of light users' preferences by linking them with heavy users. By integrating user-interest attention, our approach allows a more personalized similarity metric, adept at capturing the complex dynamics of user-item interactions. The use of interest as an intermediary layer fosters a balance between scalability and expressiveness in the model. Evaluations on two public datasets reveal that our method not only achieves improved recommendation performance but also demonstrates enhanced computational efficiency compared to item-level attention models. Our approach has also been deployed in multiple products at Meta, facilitating short-form video related recommendation.

Related papers

• LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation [50.98046887582194]
  We propose an explicit and implicit multi-interest learning framework to model user interests on two levels: behavior and semantics. The proposed EIMF framework effectively and efficiently combines small models with LLM to improve the accuracy of multi-interest modeling.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• An Efficient Multi-threaded Collaborative Filtering Approach in Recommendation System [0.0]
  This research focuses on building a scalable recommendation system capable of handling numerous users efficiently. A multithreaded similarity approach is employed to achieve this, where users are divided into independent threads that run in parallel. This parallelization significantly reduces computation time compared to traditional methods, resulting in a faster, more efficient, and scalable recommendation system.
  arXiv  Detail & Related papers  (2024-09-28T06:33:18Z)
• EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile [0.2912705470788796]
  We introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases.
  arXiv  Detail & Related papers  (2024-01-18T02:48:06Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• Knowledge-aware Coupled Graph Neural Network for Social Recommendation [29.648300580880683]
  We propose a Knowledge-aware Coupled Graph Neural Network (KCGN) that injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. We further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns.
  arXiv  Detail & Related papers  (2021-10-08T09:13:51Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• Learning User Representations with Hypercuboids for Recommender Systems [26.80987554753327]
  Our model explicitly models user interests as a hypercuboid instead of a point in the space. We present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests. A neural architecture is also proposed to facilitate user hypercuboid learning by capturing the activity sequences (e.g., buy and rate) of users.
  arXiv  Detail & Related papers  (2020-11-11T12:50:00Z)
• Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents [35.2098736872247]
  We propose a self-supervised contrastive learning approach to learn user-agent interactions. We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction. We also propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes.
  arXiv  Detail & Related papers  (2020-10-21T18:10:58Z)
• Optimizing Interactive Systems via Data-Driven Objectives [70.3578528542663]
  We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System (ISO), a novel algorithm that uses these inferred objectives for optimization.
  arXiv  Detail & Related papers  (2020-06-19T20:49:14Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.