Comments (1)
・評価をしていない
・通常のItem-based collaborative filteringの結果に加えて,taxonomyのassociation rule mining (あるtaxonomy t1に興味がある人が,t2にも興味がある確率を獲得する)を行い,このassociation rule miningの結果をCFと組み合わせて,noveltyのある推薦をしようという話(従来のHybrid Recommender Systemsでは,contents-basedの手法を使うときはitem content similarityを使うことが多い.まあこれはよくあるcontents-basedなアプローチだろう).
・documentの中のどの部分がnovelなのかとかを同定しているわけではない.taxonomyの観点からnovelだということ.
from paper_notes.
Related Issues (20)
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models, S. M Towhidul Islam Tonmoy+, N/A, arXiv'24
- Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models, Zixiang Chen+, N/A, arXiv'24
- LLaMA Pro: Progressive LLaMA with Block Expansion, Chengyue Wu+, N/A, arXiv'24 HOT 1
- Large Language Models Are State-of-the-Art Evaluators of Translation Quality, EAMT'23 HOT 1
- Experts, errors, and context: A large-scale study of human evaluation for machine translation, TACL'21 HOT 1
- BLEU might be Guilty but References are not Innocent, EMNLP'20 HOT 1
- G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment, Yang Liu+, N/A, arXiv'23 HOT 1
- INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback, Wenda Xu+, N/A, arXiv'23 HOT 2
- MM-LLMs: Recent Advances in MultiModal Large Language Models, Duzhen Zhang+, N/A, arXiv'24
- RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval, Parth Sarthi+, N/A, arXiv'24
- Self-Discover: Large Language Models Self-Compose Reasoning Structures, Pei Zhou+, N/A, arXiv'24
- Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks, Jongho Park+, N/A, arXiv'24
- RAGの性能を改善するための8つの戦略 HOT 1
- Scaling Laws for Fine-Grained Mixture of Experts, Jakub Krajewski+, N/A, arXiv'24
- The Consensus Game: Language Model Generation via Equilibrium Search, Athul Paul Jacob+, N/A, arXiv'23
- Dense Text Retrieval based on Pretrained Language Models: A Survey, Wayne Xin Zhao+, N/A, arXiv'22
- awesome-generative-information-retrieval
- Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance, Ziqi Yin+, N/A, arXiv'24
- User-LLM: Efficient LLM Contextualization with User Embeddings, Lin Ning+, N/A, arXiv'24 HOT 1
- Linear Transformers are Versatile In-Context Learners, Max Vladymyrov+, N/A, arXiv'24
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from paper_notes.