LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation

• URL: http://arxiv.org/abs/2411.09410v3
• Date: Wed, 07 May 2025 12:33:27 GMT
• Title: LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation
• Authors: Shutong Qiao, Chen Gao, Wei Yuan, Yong Li, Hongzhi Yin,
• Abstract summary: We propose a novel multi-interest SR framework combining implicit behavioral and explicit semantic perspectives.<n>It includes two modules: the Implicit Behavioral Interest Module and the Explicit Semantic Interest Module.<n>Experiments on four real-world datasets validate the framework's effectiveness and practicality.
• Score: 54.396000434574454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequential recommendation (SR) leverages users' dynamic preferences, with recent advances incorporating multi-interest learning to model diverse user interests. However, most multi-interest SR models rely on noisy, sparse implicit feedback, limiting recommendation accuracy. Large language models (LLMs) offer robust reasoning on low-quality data but face high computational costs and latency challenges for SR integration. We propose a novel LLM-based multi-interest SR framework combining implicit behavioral and explicit semantic perspectives. It includes two modules: the Implicit Behavioral Interest Module (IBIM), which learns from user behavior using a traditional SR model, and the Explicit Semantic Interest Module (ESIM), which uses clustering and prompt-engineered LLMs to extract semantic multi-interest representations from informative samples. Semantic insights from ESIM enhance IBIM's behavioral representations via modality alignment and semantic prediction tasks. During inference, only IBIM is used, ensuring efficient, LLM-free recommendations. Experiments on four real-world datasets validate the framework's effectiveness and practicality.

Related papers

• LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation [12.89199121698673]
  Large language models (LLMs) show significant potential for multi-interest analysis due to their extensive knowledge and powerful reasoning capabilities.<n>We propose an LLM-driven dual-level multi-interest modeling framework for more effective recommendation.<n> Experiments on real-world datasets show the superiority of our approach against state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-15T02:13:54Z)
• DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation [83.21140655248624]
  Large language models (LLMs) have been introduced into recommender systems (RSs)<n>We propose DeepRec, a novel LLM-based RS that enables autonomous multi-turn interactions between LLMs and TRMs for deep exploration of the item space.<n> Experiments on public datasets demonstrate that DeepRec significantly outperforms both traditional and LLM-based baselines.
  arXiv  Detail & Related papers  (2025-05-22T15:49:38Z)
• Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation [67.84581846180458]
  Session-based recommendation (SBR) predicts the next item based on anonymous sessions.<n>Recent Multimodal SBR methods utilize simplistic pre-trained models for modality learning but have limitations in semantic richness.<n>We propose a multimodal LLM-enhanced framework TPAD, which extends a distillation paradigm to decouple and align transitional patterns for promoting MSBR.
  arXiv  Detail & Related papers  (2025-04-13T07:49:08Z)
• Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation [17.18176550968383]
  We propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL), which leverages semantic information to improve the reliability of contrastive samples. SRA-CL comprises two main components: (1) Cross-Sequence Contrastive Learning via User Semantic Retrieval, which utilizes large language models (LLMs) to understand diverse user preferences and retrieve semantically similar users to form reliable positive samples through a learnable sample method; and (2) Intra-Sequence Contrastive Learning via Item Semantic Retrieval, which employs LLMs to comprehend items and retrieve similar items to perform semantic-based item substitution
  arXiv  Detail & Related papers  (2025-03-06T07:25:19Z)
• New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration [49.180693704510006]
  Referring Expression (REC) is a cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding. We introduce a new REC dataset with two key features. First, it is designed with controllable difficulty levels, requiring fine-grained reasoning across object categories, attributes, and relationships. Second, it incorporates negative text and images generated through fine-grained editing, explicitly testing a model's ability to reject non-existent targets.
  arXiv  Detail & Related papers  (2025-02-27T13:58:44Z)
• 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)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation [57.49045064294086]
  Large Language Model (LLM) has the ability to capture semantic relationships between items, independent of their popularity.<n>We introduce LLMEmb, a novel method leveraging LLM to generate item embeddings that enhance Sequential Recommender Systems (SRS) performance.
  arXiv  Detail & Related papers  (2024-09-30T03:59:06Z)
• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting [23.61061000692023]
  This study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions. We propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with Large Language Models.
  arXiv  Detail & Related papers  (2024-08-18T11:07:38Z)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)
• DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation [7.914816884185941]
  Sequential recommendation (SR) tasks aim to predict users' next interaction by learning their behavior sequence and capturing the connection between users' past interactions and their changing preferences.<n> Conventional SR models often focus solely on capturing sequential patterns within the training data, neglecting the broader context and semantic information embedded in item titles from external sources.<n>Large language models (LLMs) have recently shown promise in SR tasks due to their advanced understanding capabilities and strong generalization abilities.
  arXiv  Detail & Related papers  (2024-06-17T02:47:09Z)
• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• 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)
• Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation [26.58882747016846]
  Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance.<n>We propose an LLM-enhanced SBR framework that integrates semantic and behavioral signals from multiple views.<n>In the first stage, we use multi-view prompts to infer latent user intentions at the session semantic level, supported by an intent localization module to alleviate hallucinations.<n>In the second stage, we align and unify these semantic inferences with behavioral representations, effectively merging insights from both large and small models.
  arXiv  Detail & Related papers  (2024-02-21T14:38:02Z)
• Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications [0.7832189413179361]
  Large Language Models (LLMs) excel in comprehending and generating human-like text. This paper explores strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems.
  arXiv  Detail & Related papers  (2023-11-21T02:01:01Z)
• Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation [52.89816309759537]
  Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. We propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning framework to learn shared and behavior-specific interests for different behaviors.
  arXiv  Detail & Related papers  (2022-08-03T05:28:14Z)
• Improving Multi-Interest Network with Stable Learning [13.514488368734776]
  We propose a novel multi-interest network, named DEep Stable Multi-Interest Learning (DESMIL) DESMIL tries to eliminate the influence of subtle dependencies among captured interests via learning weights for training samples. We conduct extensive experiments on public recommendation datasets, a large-scale industrial dataset and the synthetic datasets.
  arXiv  Detail & Related papers  (2022-07-14T07:49:28Z)
• Multiple Interest and Fine Granularity Network for User Modeling [3.508126539399186]
  User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue. Most existing deep-learning based approaches exploit item-ids and category-ids but neglect fine-grained features like color and mate-rial, which hinders modeling the fine granularity of users' interests. We present Multiple interest and Fine granularity Net-work (MFN), which tackle users' multiple and fine-grained interests and construct the model from both the similarity relationship and the combination relationship among the users' multiple interests.
  arXiv  Detail & Related papers  (2021-12-05T15:12:08Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

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.