Collaborative Filtering with the Simple Bayesian Classifier

Author:

Miyahara Koji,Pazzani Michael J.

Publisher

Springer Berlin Heidelberg

Reference16 articles.

1. Billsus, D. & Pazzani, M. (1998). Learning Collaborative Filters. In Proceedings of the 15 th International Conference on Machine Learning, San Francisco, CA., Morgan Kaufmann Publishers.

2. Breese, J., Heckerman, D., Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann Publisher.

3. Domingos, P. & Pazzani M. (1997). On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 29, 103–130.

4. Gupta, D., Digiovanni, M., Narita, H., Goldberg, K. (1999). Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes. Workshop on Recommender Systems Algorithms and Evaluation, 22nd International Conference on Research and Development in Information Retrieval, Berkeley, CA.

5. Herlocker, J., Konstan, J., Borchers, A., Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. In proceedings of 22 nd International Conference on Research and Development in Information Retrieval 230–237, Berkley, CA., ACM Press.

Cited by 79 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. A novel approach for hotel recommendation system based on modified KNN and naive bayes algorithm;AIP Conference Proceedings;2024

2. Recommender Systems: A Review;Journal of the American Statistical Association;2023-11-06

3. Deep recommendation system based on knowledge graph and review text;Journal of Intelligent & Fuzzy Systems;2023-11-04

4. Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System;IEEE Transactions on Knowledge and Data Engineering;2023-10-01

5. Collaborative Recommendation System For Gadgets;2023 4th International Conference for Emerging Technology (INCET);2023-05-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3