Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation

• URL: http://arxiv.org/abs/2507.04623v1
• Date: Mon, 07 Jul 2025 02:50:04 GMT
• Title: Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
• Authors: Jinpeng Chen, Jianxiang He, Huan Li, Senzhang Wang, Yuan Cao, Kaimin Wei, Zhenye Yang, Ye Ji,
• Abstract summary: Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session.<n>Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights.<n>We propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP.
• Score: 22.653549796453426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights. Some methods try to include inter-session data but struggle with noise and irrelevant information, reducing performance. Additionally, most models rely on item ID co-occurrence and overlook rich semantic details, limiting their ability to capture fine-grained item features. To address these challenges, we propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP. First, we introduce a pluggable embedding module based on large language models (LLMs) to generate high-quality semantic representations, enhancing item embeddings. Second, HIPHOP utilizes graph neural networks (GNNs) to model item transition relationships and incorporates a dynamic multi-intent capturing module to address users' diverse interests within a session. Additionally, we design a hierarchical inter-session similarity learning module, guided by user intent, to capture global and local session relationships, effectively exploring users' long-term and short-term interests. To mitigate noise, an intent-guided denoising strategy is applied during inter-session learning. Finally, we enhance the model's discriminative capability by using contrastive learning to optimize session representations. Experiments on multiple datasets show that HIPHOP significantly outperforms existing methods, demonstrating its effectiveness in improving recommendation quality. Our code is available: https://github.com/hjx159/HIPHOP.

Related papers

• SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding [64.45047674586671]
  We introduce the concept of an intention tree and propose a dataset curation pipeline.<n>We construct a sibling multimodal benchmark, SessionIntentBench, that evaluates L(V)LMs' capability on understanding inter-session intention shift.<n>With 1,952,177 intention entries, 1,132,145 session intention trajectories, and 13,003,664 available tasks mined using 10,905 sessions, we provide a scalable way to exploit the existing session data.
  arXiv  Detail & Related papers  (2025-07-27T09:04:17Z)
• Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation [20.120161603957776]
  Session-based recommendation (SBR) is mainly based on anonymous user interaction sequences.<n>This paper proposes a novel SBR approach called LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation (LLM-DMsRec)
  arXiv  Detail & Related papers  (2025-07-27T06:54:00Z)
• Enhancing User Intent for Recommendation Systems via Large Language Models [0.0]
  DUIP is a novel framework that combines LSTM networks with Large Language Models (LLMs) to dynamically capture user intent and generate personalized item recommendations.<n>Our findings suggest that DUIP is a promising approach for next-generation recommendation systems, with potential for further improvements in cross-modal recommendations and scalability.
  arXiv  Detail & Related papers  (2025-01-18T20:35:03Z)
• Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation [3.105656247358225]
  Session-based recommendation focuses on predicting the next item a user will interact with based on sequences of anonymous user sessions.<n>Most existing methods rely heavily on users' current interactions, overlooking the wealth of auxiliary information available.<n>We propose a novel model, the Multi-Graph Co-Training model (MGCOT), which leverages not only the current session graph but also similar session graphs and a global item relation graph.
  arXiv  Detail & Related papers  (2024-12-15T08:08:07Z)
• LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation [54.396000434574454]
  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.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• 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)
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Improved Representation Learning for Session-based Recommendation [0.0]
  Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions. Existing methods leverage Graph Neural Networks (GNNs) that propagate and aggregate information from neighboring nodes. We propose using a Transformer in combination with a target attentive GNN, which allows richer Representation Learning.
  arXiv  Detail & Related papers  (2021-07-04T00:57:28Z)
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [66.04457457299218]
  We propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
  arXiv  Detail & Related papers  (2020-05-06T14:17:05Z)

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.