Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning

• URL: http://arxiv.org/abs/2409.02425v1
• Date: Wed, 4 Sep 2024 04:12:22 GMT
• Title: Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning
• Authors: Shuaishuai Huang, Haowei Yang, You Yao, Xueting Lin, Yuming Tu,
• Abstract summary: This paper proposes a novel model called the Deep Adaptive Interest Network (DAIN) DAIN dynamically models users' interests while incorporating context-aware learning mechanisms to achieve precise and adaptive personalized recommendations. Experiments conducted on several public datasets demonstrate that DAIN excels in both recommendation performance and computational efficiency.
• Score: 0.3495246564946556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In personalized recommendation systems, accurately capturing users' evolving interests and combining them with contextual information is a critical research area. This paper proposes a novel model called the Deep Adaptive Interest Network (DAIN), which dynamically models users' interests while incorporating context-aware learning mechanisms to achieve precise and adaptive personalized recommendations. DAIN leverages deep learning techniques to build an adaptive interest network structure that can capture users' interest changes in real-time while further optimizing recommendation results by integrating contextual information. Experiments conducted on several public datasets demonstrate that DAIN excels in both recommendation performance and computational efficiency. This research not only provides a new solution for personalized recommendation systems but also offers fresh insights into the application of context-aware learning in recommendation systems.

Related papers

• Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model [0.3495246564946556]
  This paper designs and analyzes a personalized recommendation system based on a dynamic user interest model. The system captures user behavior data, constructs a dynamic user interest model, and combines multiple recommendation algorithms to provide personalized content to users.
  arXiv  Detail & Related papers  (2024-10-13T17:08:16Z)
• Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation [51.25461871988366]
  We propose a sequential recommendation algorithm based on a pre-trained language model and knowledge distillation. The proposed algorithm enhances recommendation accuracy and provide timely recommendation services.
  arXiv  Detail & Related papers  (2024-09-23T08:39:07Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning [0.3058685580689604]
  This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture. The proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.
  arXiv  Detail & Related papers  (2021-10-28T13:11:29Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation [27.17948754183511]
  Reinforcement learning has shown great promise in optimizing long-term user interest in recommender systems. Existing RL-based recommendation methods need a large number of interactions for each user to learn a robust recommendation policy. We propose a meta-level model-based reinforcement learning approach for fast user adaptation.
  arXiv  Detail & Related papers  (2020-12-04T08:58:35Z)
• Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
  We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
  arXiv  Detail & Related papers  (2020-11-04T12:12:25Z)
• 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)
• Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation [49.32287384774351]
  Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. We propose Knowledge-Guided deep Reinforcement learning to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation.
  arXiv  Detail & Related papers  (2020-04-17T05:26:47Z)
• Developing a Recommendation Benchmark for MLPerf Training and Inference [16.471395965484145]
  We aim to define an industry-relevant recommendation benchmark for theerferf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets.
  arXiv  Detail & Related papers  (2020-03-16T17:13:00Z)

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.