Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires combination with sparse retrieval. In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in t
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
https://www2024.thewebconf.org/docs/tutorial-slides/large-language-models-for-recommendation.pdf
Large language models for user interest journeys
Long-Term Value of Exploration: Measurements, Findings and Algorithms
FineRec:Exploring Fine-grained Sequential Recommendation
EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention
Netflix Research
Search Result Diversification Using Query Aspects as Bottlenecks
https://dl.acm.org/doi/abs/10.1145/3477495.3532067
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
https://dl.acm.org/doi/abs/10.1145/3626772.3657691
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce