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Found 16 papers, 9 papers with code
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.
EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation
To address these challenges and provide a more comprehensive understanding of user affective experience and cognitive activity, we propose EEG-SVRec, the first EEG dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation.
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain.
Sequential Recommendation with Latent Relations based on Large Language Model
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
A Situation-aware Enhancer for Personalized Recommendation
In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions.
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
This paper emphasizes the importance of dedicated analyses and methods for domain-specific characteristics for the recommender system studies.
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.
Intersectional Two-sided Fairness in Recommendation
Fairness of recommender systems (RS) has attracted increasing attention recently.
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples.
Intent-aware Ranking Ensemble for Personalized Recommendation
To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned.
A Review-aware Graph Contrastive Learning Framework for Recommendation
Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?
ProFairRec: Provider Fairness-aware News Recommendation
To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.
A Neural Influence Diffusion Model for Social Recommendation
The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.
