FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems-Reference-Cited by-同舟云学术

FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems

Published:2016 Issue: Volume: Page:98-110
ISSN:0302-9743
Container-title:Web Technologies and Applications
language:
Short-container-title:

Author:

Xin Xin,Wang Dong,Ding Yue,Lini Chen

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-45817-5_8

Reference20 articles.

1. Ning, X., Karypis, G.: Slim: sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE (2011)

2. Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)

3. Ning, X., Karypis, G.: Sparse linear methods with side information for top-n recommendations. In: Proceedings of the Sixth ACM Conference on Recommender Systems. ACM (2012)

4. Lecture Notes in Computer Science;E Christakopoulou,2014

5. Cheng, Y., Li’ang, Y., Yong, Y.: LorSLIM: low rank sparse linear methods for top-n recommendations. In: 2014 IEEE International Conference on Data Mining (ICDM). IEEE (2014)

Cited by 2 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. ÖNERİ SİSTEMLERİNDE KULLANILAN PERFORMANS METRİKLERİNİN FİLTRELEME TEKNOLOJİLERİNE GÖRE DEĞERLENDİRİLMESİ: İŞ ÖNERİ SİSTEMLERİ ALANI ÜZERİNE BİR ARAŞTIRMA ÇALIŞMASI;Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi;2024-09-03

2. Relational Collaborative Filtering;Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval;2019-07-18