Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context

• URL: http://arxiv.org/abs/2404.09709v3
• Date: Tue, 30 Apr 2024 01:48:18 GMT
• Title: Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
• Authors: Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang,
• Abstract summary: We develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet) SFPNet designs a kind of fine-grained method for multi-scenario personalized recommendations.
• Score: 3.7566162903515115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations. Specifically, SFPNet comprises a series of blocks named as Scenario-Tailoring Block, stacked sequentially. Each block initially deploys a parameter personalization unit to integrate scenario information at a coarse-grained level by redefining fundamental features. Subsequently, we consolidate scenario-adaptively adjusted feature representations to serve as context information. By employing residual connection, we incorporate this context into the representation of each historical behavior, allowing for context-aware fine-grained customization of the behavior representations at the scenario-level, which in turn supports scenario-aware user interest modeling.

Related papers

• PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching [38.829190984763294]
  Key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario.<n>We propose PERSCEN, an innovative approach that incorporates user-specific modeling into multi-scenario matching.<n>PERSCEN constructs a user-specific feature graph based on user characteristics and employs a lightweight graph neural network to capture higher-order interaction patterns.
  arXiv  Detail & Related papers  (2025-06-23T08:15:16Z)
• Variability-Driven User-Story Generation using LLM and Triadic Concept Analysis [79.17703556418292]
  A widely used Agile practice for requirements is to produce a set of user stories (also called agile product backlog'') In the context of Software Product Lines, the requirements for a family of similar systems is thus a family of user-story sets, one per system. We combine Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompting to suggest the user-story set required to develop a new system.
  arXiv  Detail & Related papers  (2025-04-11T16:15:27Z)
• From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment [41.96246165999026]
  Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs.
  arXiv  Detail & Related papers  (2025-03-19T17:41:46Z)
• Test-Time Alignment via Hypothesis Reweighting [56.71167047381817]
  Large pretrained models often struggle with underspecified tasks. We propose a novel framework to address the challenge of aligning models to test-time user intent.
  arXiv  Detail & Related papers  (2024-12-11T23:02:26Z)
• Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation [11.489766641148151]
  Multi-scenario Recommendation (MSR) is an important topic that improves ranking performance by leveraging information from different scenarios. Recent methods for MSR mostly construct scenario shared or specific modules to model commonalities and differences among scenarios. We propose a unified model named Cross-Scenario Information Interaction (CSII) to serve all scenarios by a mixture of scenario-dominated experts.
  arXiv  Detail & Related papers  (2024-07-29T06:17:33Z)
• UMSE: Unified Multi-scenario Summarization Evaluation [52.60867881867428]
  Summarization quality evaluation is a non-trivial task in text summarization. We propose Unified Multi-scenario Summarization Evaluation Model (UMSE) Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios.
  arXiv  Detail & Related papers  (2023-05-26T12:54:44Z)
• SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment [5.834783927354705]
  A novel framework called Scenario and Model Associative percepts (SMAP) is developed. SMAP can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. A novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching.
  arXiv  Detail & Related papers  (2023-05-23T03:01:26Z)
• Application-Driven AI Paradigm for Person Counting in Various Scenarios [2.2881898195409884]
  We propose a person counting paradigm that utilizes a scenario classifier to automatically select a suitable person counting model for each captured frame. We present five augmentation datasets collected from different scenarios, including side-view, long-shot, top-view, customized and crowd.
  arXiv  Detail & Related papers  (2023-03-24T03:57:21Z)
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation [35.4495536683099]
  We propose a scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests.
  arXiv  Detail & Related papers  (2022-08-24T11:44:00Z)
• PinnerFormer: Sequence Modeling for User Representation at Pinterest [60.335384724891746]
  We introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action.
  arXiv  Detail & Related papers  (2022-05-09T18:26:51Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing [110.4684789199555]
  We introduce scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules. Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios.
  arXiv  Detail & Related papers  (2022-02-02T08:00:21Z)
• Sequence Adaptation via Reinforcement Learning in Recommender Systems [8.909115457491522]
  We propose the SAR model, which learns the sequential patterns and adjusts the sequence length of user-item interactions in a personalized manner. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches.
  arXiv  Detail & Related papers  (2021-07-31T13:56:46Z)
• Set Based Stochastic Subsampling [85.5331107565578]
  We propose a set-based two-stage end-to-end neural subsampling model that is jointly optimized with an textitarbitrary downstream task network. We show that it outperforms the relevant baselines under low subsampling rates on a variety of tasks including image classification, image reconstruction, function reconstruction and few-shot classification.
  arXiv  Detail & Related papers  (2020-06-25T07:36:47Z)
• Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem [55.21502268698577]
  This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
  arXiv  Detail & Related papers  (2020-03-30T09:35:12Z)

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.