# MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment

arXiv: https://arxiv.org/abs/2503.01711 · HTML: https://mlsanity.com/paper/2503.01711/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Large Language Models Empowered Personalized Web Agents](https://mlsanity.com/paper/2410.17236/) (2024)
- [CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search](https://mlsanity.com/paper/2406.05013/) (2024)
- [LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://mlsanity.com/paper/2405.03988/) (2024)
- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [A Transformer-based Embedding Model for Personalized Product Search](https://mlsanity.com/paper/2005.08936/) (2020)
- [A Zero Attention Model for Personalized Product Search](https://mlsanity.com/paper/1908.11322/) (2019)
- [KuaiSAR: A Unified Search And Recommendation Dataset](https://mlsanity.com/paper/2306.07705/) (2023)
- [UniSAR: Modeling User Transition Behaviors between Search and Recommendation](https://mlsanity.com/paper/2404.09520/) (2024)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation](https://mlsanity.com/paper/2308.08497/) (2023)
- [JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions](https://mlsanity.com/paper/2305.14810/) (2023)
- [Explainable Product Search with a Dynamic Relation Embedding Model](https://mlsanity.com/paper/1909.07212/) (2019)
- [UnifiedSSR: A Unified Framework of Sequential Search and Recommendation](https://mlsanity.com/paper/2310.13921/) (2023)

## Built on by (most cited first, 3 of 3)

- [Structuring the Space of Sociotechnical Alignment](https://mlsanity.com/paper/2607.01250/) (2026)
- [GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items](https://mlsanity.com/paper/2603.14259/) (2026)
- [IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters](https://mlsanity.com/paper/2604.18375/) (2026)

## Abstract (arXiv)

Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that users' query fully captures their real motivation. However, our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before searching, indicating they refine intents through consultations based on motivation and need. The implied motivation in consultations is a key enhancing factor for personalized search. This unexplored area comes with new challenges including aligning contextual motivations with concise queries, bridging the category-text gap, and filtering noise within sequence history. To address these, we propose a Motivation-Aware Personalized Search (MAPS) method. It embeds queries and consultations into a unified semantic space via LLMs, utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics, and introduces dual alignment: (1) contrastive learning aligns consultations, reviews, and product features; (2) bidirectional attention integrates motivation-aware embeddings with user preferences. Extensive experiments on real and synthetic data show MAPS outperforms existing methods in both retrieval and ranking tasks.