Papers Session-Based Recommendations
“Session-Based Recommendations” 태그가 달린 논문 173편 · 필터 해제
SemSR: Semantics aware robust Session-based Recommendations
Session-based recommendation (SR) models aim to recommend items to anonymous users based on their behavior during the current session. While various SR models in the literature utilize item sequences to predict the next …
Session-Based RecommendationsHierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
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 inform…
Contrastive LearningDenoisingSession-Based RecommendationsRethinking Contrastive Learning in Session-based Recommendation
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that exi…
Contrastive LearningSelf-Supervised LearningSession-Based RecommendationsA Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts incorporate various side information to al…
Session-Based RecommendationsSurveyLinear Item-Item Model with Neural Knowledge for Session-based Recommendation
Session-based recommendation (SBR) aims to predict users' subsequent actions by modeling short-term interactions within sessions. Existing neural models primarily focus on capturing complex dependencies for sequential it…
Session-Based RecommendationsDistilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To further alleviate data sparsity and cold-st…
Mutual Information EstimationSession-Based RecommendationsUnsupervised Graph Embeddings for Session-based Recommendation with Item Features
In session-based recommender systems, predictions are based on the user's preceding behavior in the session. State-of-the-art sequential recommendation algorithms either use graph neural networks to model sessions in a g…
Recommendation SystemsSequential RecommendationSession-Based RecommendationsSPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning
Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limite…
Graph LearningSession-Based RecommendationsMulti-Graph Co-Training for Capturing User Intent in Session-based Recommendation
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 data sparsity due to the typically short-…
Contrastive LearningSession-Based RecommendationsActive Large Language Model-based Knowledge Distillation for Session-based Recommendation
Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate the…
Active LearningKnowledge DistillationLanguage ModelingLanguage Modelling+2Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), e…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingRecommendation Systems+1GRAINRec: Graph and Attention Integrated Approach for Real-Time Session-Based Item Recommendations
Recent advancements in session-based recommendation models using deep learning techniques have demonstrated significant performance improvements. While they can enhance model sophistication and improve the relevance of r…
Session-Based RecommendationsGuided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation
Session-based recommendation (SR) models aim to recommend top-K items to a user, based on the user's behaviour during the current session. Several SR models are proposed in the literature, however,concerns have been rais…
counterfactualData AugmentationSession-Based RecommendationsOptimizing Encoder-Only Transformers for Session-Based Recommendation Systems
Session-based recommendation is the task of predicting the next item a user will interact with, often without access to historical user data. In this work, we introduce Sequential Masked Modeling, a novel approach for en…
Data AugmentationRecommendation SystemsSession-Based RecommendationsEnhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
Session-based Recommendation (SBR), seeking to predict a user's next action based on an anonymous session, has drawn increasing attention for its practicability. Most SBR models only rely on the contextual transitions wi…
AttributeGraph Neural NetworkSession-Based RecommendationsItem Cluster-aware Prompt Learning for Session-based Recommendation
Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly on intra-session item relationships, negl…
Prompt LearningSession-Based RecommendationsEnhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent mann…
Contrastive LearningMusic RecommendationSequential RecommendationSession-Based RecommendationsIncorporating Like-Minded Peers to Overcome Friend Data Sparsity in Session-Based Social Recommendations
Session-based Social Recommendation (SSR) leverages social relationships within online networks to enhance the performance of Session-based Recommendation (SR). However, existing SSR algorithms often encounter the challe…
Graph AttentionSession-Based RecommendationsGraph 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 recommendation (SR) specializes in users' short-…
Graph Neural NetworkRecommendation SystemsSession-Based RecommendationsA GNN Model with Adaptive Weights for Session-Based Recommendation Systems
Session-based recommendation systems aim to model users' interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in sess…
Graph Neural NetworkRecommendation SystemsSession-Based Recommendations