GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems

• URL: http://arxiv.org/abs/2410.20643v2
• Date: Thu, 13 Mar 2025 00:54:57 GMT
• Title: GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems
• Authors: Wilson Wongso, Hao Xue, Flora D. Salim,
• Abstract summary: Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability.<n>Existing methods often address this by leveraging similar trajectories from other users.<n>We propose a method that generates natural language (NL) user profiles from large-scale, location-based social network (LBSN) check-ins.
• Score: 8.789624590579903
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traditional Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings. Furthermore, the cold-start problem -- where systems have insufficient data for new users -- limits their ability to generate accurate recommendations. Existing methods often address this by leveraging similar trajectories from other users, but this approach can be computationally expensive and increases the context length for LLM-based methods, making them difficult to scale. To address these limitations, we propose a method that generates natural language (NL) user profiles from large-scale, location-based social network (LBSN) check-ins, utilizing robust personality assessments and behavioral theories. These NL profiles capture user preferences, routines, and behaviors, improving POI prediction accuracy while offering enhanced transparency. By incorporating NL profiles as system prompts to LLMs, our approach reduces reliance on extensive historical data, while remaining flexible, easily updated, and computationally efficient. Our method is not only competitive with other LLM-based and complex agentic frameworks but is also more scalable for real-world POI recommender systems. Results demonstrate that our approach consistently outperforms baseline methods, offering a more interpretable and resource-efficient solution for POI recommendation systems. Our source code is available at: https://github.com/w11wo/GenUP/.

Related papers

• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Large Language Model driven Policy Exploration for Recommender Systems [50.70228564385797]
  offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Online RL-based RS also face challenges in production deployment due to the risks of exposing users to untrained or unstable policies. Large Language Models (LLMs) offer a promising solution to mimic user objectives and preferences for pre-training policies offline. We propose an Interaction-Augmented Learned Policy (iALP) that utilizes user preferences distilled from an LLM.
  arXiv  Detail & Related papers  (2025-01-23T16:37:44Z)
• RecLM: Recommendation Instruction Tuning [17.780484832381994]
  We propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed $underlineRec$ommendation enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function.
  arXiv  Detail & Related papers  (2024-12-26T17:51:54Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications. Ensuring their alignment with the diverse preferences of individual users has become a critical challenge. We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• Incorporate LLMs with Influential Recommender System [34.5820082133773]
  proactive recommender systems recommend a sequence of items to guide user interest in the target item. Existing methods struggle to construct a coherent influence path that builds up with items the user is likely to enjoy. We introduce a novel approach named LLM-based Influence Path Planning (LLM-IPP) Our approach maintains coherence between consecutive recommendations and enhances user acceptability of the recommended items.
  arXiv  Detail & Related papers  (2024-09-07T13:41:37Z)
• Data Imputation using Large Language Model to Accelerate Recommendation System [3.853804391135035]
  We propose a novel approach that fine-tune Large Language Model (LLM) and use it impute missing data for recommendation systems. LLM which is trained on vast amounts of text, is able to understand complex relationship among data and intelligently fill in missing information. This enriched data is then used by the recommendation system to generate more accurate and personalized suggestions.
  arXiv  Detail & Related papers  (2024-07-14T04:53:36Z)
• Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
  Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
  arXiv  Detail & Related papers  (2024-07-02T10:09:19Z)
• ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation [38.64175351885443]
  Large language models have been flourishing in the natural language processing (NLP) domain. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it.
  arXiv  Detail & Related papers  (2024-06-27T01:37:57Z)
• Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
  We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
  arXiv  Detail & Related papers  (2024-05-31T14:21:04Z)
• Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations [11.004673022505566]
  Long user queries from millions of users can degrade the performance of large language models for recommendation. We propose a hybrid task allocation framework that utilizes the capabilities of both large language models and traditional recommendation systems. Our results on three real-world datasets show a significant reduction in weak users and improved robustness of RSs to sub-populations.
  arXiv  Detail & Related papers  (2024-05-01T19:11:47Z)
• Fine-Tuning Language Models with Reward Learning on Policy [68.70065254564642]
  Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. Despite its popularity, (fixed) reward models may suffer from inaccurate off-distribution. We propose reward learning on policy (RLP), an unsupervised framework that refines a reward model using policy samples to keep it on-distribution.
  arXiv  Detail & Related papers  (2024-03-28T10:02:10Z)
• Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts [95.09994361995389]
  Relative Preference Optimization (RPO) is designed to discern between more and less preferred responses derived from both identical and related prompts. RPO has demonstrated a superior ability to align large language models with user preferences and to improve their adaptability during the training process.
  arXiv  Detail & Related papers  (2024-02-12T22:47:57Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach [56.12815715932561]
  We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
  arXiv  Detail & Related papers  (2022-04-20T01:25:08Z)
• Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL [56.20835219296896]
  We study session-based recommendation scenarios where we want to recommend items to users during sequential interactions to improve their long-term utility. We develop a new batch RL algorithm called Short Horizon Policy Improvement (SHPI) that approximates policy-induced distribution shifts across sessions.
  arXiv  Detail & Related papers  (2021-06-01T15:58:05Z)

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.