Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

Author:

Anava Oren1,Golan Shahar2,Golbandi Nadav2,Karnin Zohar2,Lempel Ronny3,Rokhlenko Oleg2,Somekh Oren2

Affiliation:

1. Technion, Haifa, Israel

2. Yahoo Labs, Haifa, Israel

3. Outbrain Inc., Netanya, Israel

Publisher

International World Wide Web Conferences Steering Committee

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dynamic Length Factorization Machines for CTR Prediction;2021 IEEE International Conference on Big Data (Big Data);2021-12-15

2. A prediction-oriented optimal design for visualisation recommender systems;Statistical Theory and Related Fields;2021-03-30

3. Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations;Data Mining and Knowledge Discovery;2020-08-03

4. MAMO;Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2020-07-06

5. Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning;IEEE Transactions on Knowledge and Data Engineering;2020-04-01

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