Search Results for author:
Found 23 papers, 15 papers with code
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
Neural Logic Reasoning
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
A Survey on Dropout Methods and Experimental Verification in Recommendation
Among various methods of coping with overfitting, dropout is one of the representative ways.
A Survey on the Fairness of Recommender Systems
First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues.
Towards Representation Alignment and Uniformity in Collaborative Filtering
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
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.
Measuring Item Global Residual Value for Fair Recommendation
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
Intersectional Two-sided Fairness in Recommendation
Fairness of recommender systems (RS) has attracted increasing attention recently.
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.
Multi-Agent Collaboration Framework for Recommender Systems
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks.
EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems
Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly recognized for their ability to improve long-term user engagement.
Citation-Enhanced Generation for LLM-based Chatbots
Note that our method is a training-free plug-and-play plugin that is capable of various LLMs.
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
First, we propose and define the recommendability identification task, which investigates the need for recommendations in the current conversational context.
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.
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.
