Recommender System for Academic Literature with Incremental Dataset-Reference-Cited by-同舟云学术

Recommender System for Academic Literature with Incremental Dataset

Published:2016 Issue: Volume:89 Page:483-491
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Dhanda Mahak,Verma Vijay

Publisher

Elsevier BV

Subject

General Engineering

Reference16 articles.

1. Fournier-Viger and Philippe, Efficient Incremental High Utility Itemset Mining, In Proceedings of the ASE BigData & SocialInformatics, pp. 53, (2015).

2. Y. Liu, W. Liao and A. Choudhary, A Fast High Utility Itemsets Mining Algorithm, In Proceedings of the 1st International Workshop on Utility-based Data Mining, pp. 90-99, (2005).

3. Shenshen Liang, Ying Liu, Liheng Jian and Yang Gao, A Utility-based Recommendation Approach for Academic Literatures, In ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 229-232, (2011).

4. Sugiyama, Kazunari and Min-Yen Kan, Scholarly Paper Recommendation Via user's Recent Research Interests, In Proceedings of the 10th Annual Joint Conference on Digital Libraries, pp. 29-38, (2010).

5. de Gemmis and Marco, Semantics-Aware Content-Based Recommender Systems, In Recommender Systems Handbook, pp. 119-159, (2015).

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