Agentic Personalized Fashion Recommendation in the Age of Generative AI: Challenges, Opportunities, and Evaluation

• URL: http://arxiv.org/abs/2508.02342v1
• Date: Mon, 04 Aug 2025 12:22:25 GMT
• Title: Agentic Personalized Fashion Recommendation in the Age of Generative AI: Challenges, Opportunities, and Evaluation
• Abstract summary: This paper synthesizes both academic and industrial viewpoints to map the distinctive output space and stakeholder ecosystem of modern FaRS.<n>We propose an Agentic Mixed-Modality Refinement pipeline, which fuses multimodal encoders with agentic LLM planners and dynamic retrieval.
• Score: 9.319920301747297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fashion recommender systems (FaRS) face distinct challenges due to rapid trend shifts, nuanced user preferences, intricate item-item compatibility, and the complex interplay among consumers, brands, and influencers. Traditional recommendation approaches, largely static and retrieval-focused, struggle to effectively capture these dynamic elements, leading to decreased user satisfaction and elevated return rates. This paper synthesizes both academic and industrial viewpoints to map the distinctive output space and stakeholder ecosystem of modern FaRS, identifying the complex interplay among users, brands, platforms, and influencers, and highlighting the unique data and modeling challenges that arise. We outline a research agenda for industrial FaRS, centered on five representative scenarios spanning static queries, outfit composition, and multi-turn dialogue, and argue that mixed-modality refinement-the ability to combine image-based references (anchors) with nuanced textual constraints-is a particularly critical task for real-world deployment. To this end, we propose an Agentic Mixed-Modality Refinement (AMMR) pipeline, which fuses multimodal encoders with agentic LLM planners and dynamic retrieval, bridging the gap between expressive user intent and fast-changing fashion inventories. Our work shows that moving beyond static retrieval toward adaptive, generative, and stakeholder-aware systems is essential to satisfy the evolving expectations of fashion consumers and brands.

Related papers

• Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis [55.78822230833247]
  We present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis.<n>Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations.<n>We propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimize interaction reconstruction and semantic understanding.
  arXiv  Detail & Related papers  (2026-08-20T17:51:48Z)
• Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory [54.947805187562274]
  We introduce realistic dialogues across diverse interaction scenarios that exhibit dynamic temporal evolution and long-term coherence.<n>The resulting benchmark encompasses challenging question-answer pairs spanning seven inquiry types.<n>We identify at least one of 27 critical memory characteristics that we identify as essential yet underexplored in current research.
  arXiv  Detail & Related papers  (2026-05-29T09:51:22Z)
• AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition [62.16431420189863]
  LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states.<n>We introduce AffectAgent, an affect-oriented multi-agent retrieval-augmented generation framework.<n>AffectAgent comprises three jointly optimized agents, namely a query planner, an evidence filter, and an emotion generator.
  arXiv  Detail & Related papers  (2026-04-14T13:49:19Z)
• CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation [23.478610632707728]
  We propose a Category-guided Attentive Mixture of Experts model for Multimodal Sequential Recommendation.<n>At its core, CAMMSR introduces a category-guided attentive mixture of experts module, which learns specialized item representations from multiple perspectives.<n>Experiments on four public datasets demonstrate that CAMMSR consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-04T17:39:35Z)
• Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning [56.24016465596292]
  A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric.<n>We introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the "creative essence" from a reference image and re-materialize that abstract logic onto a user-specified subject.<n>Our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media.
  arXiv  Detail & Related papers  (2026-02-01T17:01:36Z)
• AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts [78.33143446024485]
  We introduce textbfAgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles.<n>This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios.
  arXiv  Detail & Related papers  (2026-01-28T16:05:44Z)
• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
  We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
  arXiv  Detail & Related papers  (2025-10-19T16:46:11Z)
• Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation [1.864621482724548]
  This research introduces a new framework named FGAT, which leverages a hierarchical graph representation together with graph attention mechanisms to address this problem.<n>The framework constructs a three-tier graph of users, outfits, and items, integrating visual and textual features to jointly model outfit compatibility and user preferences.<n>FGAT outperforms strong baselines such as HFGN, achieving notable improvements in accuracy, precision, HR, recall, and NDCG.
  arXiv  Detail & Related papers  (2025-08-14T23:09:57Z)
• AI Guided Accelerator For Search Experience [4.832123045961485]
  We propose a novel framework that explicitly models transitional queries--intermediate reformulations occurring during the user's journey toward their final purchase intent.<n>This approach allows us to model a user's shopping funnel, where mid-journey transitions reflect exploratory behavior and intent refinement.<n>Our contributions include (i) the formal identification and modeling of transitional queries, (ii) the introduction of a structured query sequence mining pipeline for intent flow understanding, and (iii) the application of LLMs for scalable, intent-aware query expansion.
  arXiv  Detail & Related papers  (2025-07-25T23:26:00Z)
• Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent [56.61028117645315]
  We propose a novel thought-augmented interactive recommender agent system (TAIRA) that addresses complex user intents through distilled thought patterns.<n>Specifically, TAIRA is designed as an LLM-powered multi-agent system featuring a manager agent that orchestrates recommendation tasks by decomposing user needs and planning subtasks.<n>Through comprehensive experiments conducted across multiple datasets, TAIRA exhibits significantly enhanced performance compared to existing methods.
  arXiv  Detail & Related papers  (2025-06-30T03:15:50Z)
• Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest [9.904093205817247]
  The retrieval stage plays a critical role in generating a high-recall set of candidate items.<n>Traditional two-tower models struggle in this regard due to limited user-item feature interaction.<n>We propose a novel multi-embedding retrieval framework designed to enhance user interest representation.
  arXiv  Detail & Related papers  (2025-06-29T02:14:21Z)
• From Intent Discovery to Recognition with Topic Modeling and Synthetic Data [0.0]
  Customer utterances are characterized by infrequent word co-occurences and high term variability.<n>We propose an agentic LLM framework for topic modeling and synthetic query generation.<n>We show that LLM-generated intent descriptions and keywords can effectively substitute for human-curated versions.
  arXiv  Detail & Related papers  (2025-05-16T12:20:31Z)
• Optimizing Recommendations using Fine-Tuned LLMs [0.0]
  This paper proposes an approach that generates synthetic datasets by modeling real-world user interactions.<n>It allows users to express more information with complex preferences, such as mood, plot details, and thematic elements.
  arXiv  Detail & Related papers  (2025-05-11T04:53:34Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash [0.4288177321445912]
  We introduce a joint training framework for product and user queries by aligning uni-modal and multi-modal encoders through contrastive learning on image-text data.<n>Our novel approach trains a query encoder with an LLM-curated relevance dataset, eliminating the reliance on engagement history.<n>For personalized ads recommendation, a significant uplift in the click-through rate and conversion rate after the deployment confirms the impact on key business metrics.
  arXiv  Detail & Related papers  (2025-03-18T20:38:31Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  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.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation [58.04939553630209]
  In real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as the long-tail user and long-tail item challenges, often pose difficulties for existing Sequential Recommendation systems. We propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR) to address these challenges.
  arXiv  Detail & Related papers  (2024-05-31T07:24:42Z)
• 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)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)

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.