Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …
alleviating the information overload problem. As a new paradigm of RSs, session-based …
Large language models for intent-driven session recommendations
The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …
intents within a session for accurate next-item prediction. However, the capability of these …
Disentangling id and modality effects for session-based recommendation
Session-based recommendation aims to predict intents of anonymous users based on their
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …
Enhancing Collaborative Information with Contrastive Learning for Session-based Recommendation
G An, J Sun, Y Yang, F Sun - Information Processing & Management, 2024 - Elsevier
Session-based recommendation (SBR) aims to exploit the session representation generated
by combining item embedding and session embedding processes to recommend the next …
by combining item embedding and session embedding processes to recommend the next …
LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation
Traditional session-based recommendation (SBR) utilizes session behavior sequences from
anonymous users for recommendation. Although this strategy is highly efficient, it sacrifices …
anonymous users for recommendation. Although this strategy is highly efficient, it sacrifices …
RAIN: Reconstructed-aware in-context enhancement with graph denoising for session-based recommendation
Session-based recommendation aims to recommend the next item based on short-term
interactions. Traditional session-based recommendation methods assume that all interacted …
interactions. Traditional session-based recommendation methods assume that all interacted …
Multi-behavior Hypergraph Contrastive Learning for Session-based Recommendation
L Guo, S Zhou, H Tang, X Zheng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Most current session-based recommendations model session sequences solely based on
the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors …
the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors …
LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation
Multi-interest modeling in current recommender systems (RS) is mainly based on user
behavioral data, capturing user interest preferences from multiple dimensions. However …
behavioral data, capturing user interest preferences from multiple dimensions. However …
Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation
Z Yang, T Liang - arXiv preprint arXiv:2412.11105, 2024 - arxiv.org
Session-based recommendation focuses on predicting the next item a user will interact with
based on sequences of anonymous user sessions. A significant challenge in this field is …
based on sequences of anonymous user sessions. A significant challenge in this field is …
Graph Isomorphic Network with Denoising Attention Mechanism for Session-Based Recommendation
D Lu, X Lu, Z Wang, J Huang - 2024 4th International …, 2024 - ieeexplore.ieee.org
The session-based recommendation system aims to predict items that users may interact
with in the future by analyzing historical behaviors in anonymous sessions. This field has …
with in the future by analyzing historical behaviors in anonymous sessions. This field has …