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Found 12 papers, 3 papers with code
Structured Entity Extraction Using Large Language Models
Recent advances in machine learning have significantly impacted the field of information extraction, with Large Language Models (LLMs) playing a pivotal role in extracting structured information from unstructured text.
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems.
Teacher-Student Architecture for Knowledge Distillation: A Survey
Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement.
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Representation learning has been a critical topic in machine learning.
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
Joint Multisided Exposure Fairness for Recommendation
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
