2 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Position Bias Estimation with Item Embedding for Sparse Dataset
Shion Ishikawa, Yun Ching Liu, Young-Joo Chung +1
Estimating position bias is a well-known challenge in Learning to Rank (L2R). Click data in e-commerce applications, such as targeted advertisements and search engines, provides im…
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…
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
Shion Ishikawa, Young-joo Chung, Yu Hirate
Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and e…
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…