Search Results for author:
Found 29 papers, 18 papers with code
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
HyPE leverages hierarchical category paths as explanation, progressing from broad to specific semantic categories.
Taxonomy-guided Semantic Indexing for Academic Paper Search
Academic paper search is an essential task for efficient literature discovery and scientific advancement.
SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation
Our in-depth analysis finds that there is a significant difference in the knowledge captured by the model from heterogeneous item indices and diverse input prompts, which can have a high potential for complementarity.
Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, generating significant interest in their application to recommendation systems.
Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts
In this paper, we propose ERAlign, an unsupervised and robust cross-lingual EA pipeline that jointly performs Entity-level and Relation-level Alignment by neighbor triple matching strategy using semantic textual features of relations and entities.
Knowledge Distillation Approaches for Accurate and Efficient Recommender System
This dissertation is devoted to developing knowledge distillation methods for recommender systems to fully improve the performance of a compact model.
Graph Signal Processing for Cross-Domain Recommendation
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem.
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
In this paper, we propose DynaCor framework that distinguishes incorrectly labeled instances from correctly labeled ones based on the dynamics of the training signals.
Continual Collaborative Distillation for Recommender System
We expect this research direction to contribute to narrowing the gap between existing KD studies and practical applications, thereby enhancing the applicability of KD in real-world systems.
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains.
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy
In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promising results.
Top-Personalized-K Recommendation
To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.
Deep Rating Elicitation for New Users in Collaborative Filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations.
Chain-of-Factors Paper-Reviewer Matching
To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors.
MvFS: Multi-view Feature Selection for Recommender System
Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention.
Distillation from Heterogeneous Models for Top-K Recommendation
Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.
Learning Topology-Specific Experts for Molecular Property Prediction
In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics.
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives.
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering.
Obtaining Calibrated Probabilities with Personalized Ranking Models
Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.
Unsupervised Proxy Selection for Session-based Recommender Systems
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).
Topology Distillation for Recommender System
To address this issue, we propose a novel method named Hierarchical Topology Distillation (HTD) which distills the topology hierarchically to cope with the large capacity gap.
Bidirectional Distillation for Top-K Recommender System
Recommender systems (RS) have started to employ knowledge distillation, which is a model compression technique training a compact model (student) with the knowledge transferred from a cumbersome model (teacher).
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder.
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.
One-class Classification Robust to Geometric Transformation
Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images.
DE-RRD: A Knowledge Distillation Framework for Recommender System
Recent recommender systems have started to employ knowledge distillation, which is a model compression technique distilling knowledge from a cumbersome model (teacher) to a compact model (student), to reduce inference latency while maintaining performance.
