Papers Session-Based Recommendations
“Session-Based Recommendations” 태그가 달린 논문 173편 · 필터 해제
Multi-intent-aware Session-based Recommendation
Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated neural-based encoders to learn a sessio…
Session-Based RecommendationsDisentangling 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 patterns reflected by item IDs, and fine-g…
Causal InferencecounterfactualCounterfactual InferenceDisentanglement+1A Simple Yet Effective Approach for Diversified Session-Based Recommendation
Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs primarily maximize recommendation accuracy …
DiversityRecommendation SystemsSession-Based RecommendationsRe2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR.…
Language ModelingLanguage ModellingLarge Language ModelSession-Based RecommendationsExplainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge g…
Hierarchical Reinforcement LearningKnowledge GraphsSession-Based RecommendationsSide Information-Driven Session-based Recommendation: A Survey
The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into th…
Session-Based RecommendationsSurveyIntegrating Large Language Models with Graphical Session-Based Recommendation
With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pioneering strategies have primarily transfor…
Natural Language UnderstandingRecommendation SystemsSession-Based RecommendationsSpecificity+1InteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models
Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborati…
Collaborative FilteringInteractive RecommendationNavigateRecommendation Systems+1Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation
Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance. Researchers have identified that beyond behavioral signals, rich se…
Recommendation SystemsSession-Based RecommendationsEnhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
Session-based recommendation has gained increasing attention in recent years, with its aim to offer tailored suggestions based on users' historical behaviors within sessions. To advance this field, a variety of methods h…
Session-Based RecommendationsPerformance Comparison of Session-based Recommendation Algorithms based on GNNs
In session-based recommendation settings, a recommender system has no access to long-term user profiles and thus has to base its suggestions on the user interactions that are observed in an ongoing session. Since such se…
Recommendation SystemsSession-Based RecommendationsEnhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs …
AttributeSession-Based RecommendationsOn the Effectiveness of Unlearning in Session-Based Recommendation
Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain traini…
Session-Based RecommendationsExploring Popularity Bias in Session-based Recommendation
Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback. Many adopt the \t…
Recommendation SystemsSession-Based RecommendationsSession-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, th…
DenoisingSession-Based RecommendationsContrastive Multi-Level Graph Neural Networks for Session-based Recommendation
Session-based recommendation (SBR) aims to predict the next item at a certain time point based on anonymous user behavior sequences. Existing methods typically model session representation based on simple item transition…
Contrastive LearningRelationSession-Based RecommendationsBi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unf…
Multi-Task LearningSession-Based RecommendationsTempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations
Session-based recommendations which predict the next action by understanding a user's interaction behavior with items within a relatively short ongoing session have recently gained increasing popularity. Previous researc…
Session-Based RecommendationsContext-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, mos…
Session-Based RecommendationsBeyond Co-occurrence: Multi-modal Session-based Recommendation
Session-based recommendation is devoted to characterizing preferences of anonymous users based on short sessions. Existing methods mostly focus on mining limited item co-occurrence patterns exposed by item ID within sess…
Contrastive LearningDescriptiveRepresentation LearningSession-Based Recommendations