189 citations · 584 across the 63 of their papers we have counts for
10 papers · 1 filter
NewsReX: A More Efficient Approach to News Recommendation with Keras 3 and JAX
Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
Reproducing and comparing results in news recommendation research has become increasingly difficult. This is due to a fragmented ecosystem of diverse codebases, varied configuratio…
Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings
Ziyin Xiao, Toyotaro Suzumura
Cross-domain recommender (CDR) systems aim to transfer knowledge from data-rich domains to data-sparse ones, alleviating sparsity and cold-start issues present in conventional sing…
SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation
Toyotaro Suzumura, Hisashi Ikari, Hiroki Kanezashi +2
Modern recommendation systems fuse user behavior graphs and review texts but often encounter a "Fusion Gap" caused by False Negatives, Popularity Bias, and Signal Ambiguity. We pro…
Multimodal Point-of-Interest Recommendation
Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi +2
Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based o…
A Prompting-Based Representation Learning Method for Recommendation with Large Language Models
Junyi Chen, Toyotaro Suzumura
In recent years, Recommender Systems (RS) have witnessed a transformative shift with the advent of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). M…
Popular News Always Compete for the User's Attention! POPK: Mitigating Popularity Bias via a Temporal-Counterfactual
Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
In news recommendation systems, reducing popularity bias is essential for delivering accurate and diverse recommendations. This paper presents POPK, a new method that uses temporal…
A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System
Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by th…
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Boming Yang, Dairui Liu, Toyotaro Suzumura +2
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using ad…
Exploring 360-Degree View of Customers for Lookalike Modeling
Md Mostafizur Rahman, Daisuke Kikuta, Satyen Abrol +5
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large…
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
Daisuke Kikuta, Toyotaro Suzumura, Md Mostafizur Rahman +5
Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph…