Towards a Hybrid User and Item-Based Collaborative Filtering Under the Belief Function Theory-Reference-Cited by-同舟云学术

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Towards a Hybrid User and Item-Based Collaborative Filtering Under the Belief Function Theory

Published:2018 Issue: Volume: Page:395-406
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Abdelkhalek Raoua,Boukhris Imen,Elouedi Zied

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-91473-2_34

Reference24 articles.

1. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_1

2. Su, X., Khoshgoftaar, T. M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 1–19 (2009)

3. Aggarwal, C.C.: Neighborhood-based collaborative filtering. Recommender Systems, pp. 29–70. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_2

4. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)

5. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295. ACM (2001)

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1. Dempster-Shafer Theory in Recommender Systems: A Survey;International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems;2024-07

2. Matrix decomposition model of E-commerce based on collaborative filtering algorithm;International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021);2021-12-22

3. An Evidential Collaborative Filtering Dealing with Sparsity Problem and Data Imperfections;Advances in Intelligent Systems and Computing;2019-04-14

4. Joining Items Clustering and Users Clustering for Evidential Collaborative Filtering;Intelligent Data Engineering and Automated Learning – IDEAL 2019;2019

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