9 citations · 15 across the 9 of their papers we have counts for
6 papers · 1 filter
Are Large Language Models Really Effective for Training-Free Cold-Start Recommendation?
Genki Kusano, Kenya Abe, Kunihiro Takeoka
Recommender systems usually rely on large-scale interaction data to learn from users' past behaviors and make accurate predictions. However, real-world applications often face situ…
Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input. Compared to traditional methods such as collaborative filterin…
LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries
Kenya Abe, Kunihiro Takeoka, Makoto P. Kato +1
Query expansion (QE) enhances retrieval by incorporating relevant terms, with large language models (LLMs) offering an effective alternative to traditional rule-based and statistic…
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed +11
Question Answering (QA) accounts for a significant portion of LLM usage "in the wild". However, LLMs sometimes produce false or misleading responses, also known as "hallucinations"…
Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
In large language models (LLM)-based recommendation systems (LLM-RSs), accurately predicting user preferences by leveraging the general knowledge of LLMs is possible without requir…
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
Yuyang Dong, Kunihiro Takeoka, Chuan Xiao +1
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that…