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View all- Hawashin BAlzubi SKanan TMansour A(2019)An efficient semantic recommender method forArabic textThe Electronic Library10.1108/EL-12-2018-024537:2(263-280)Online publication date: Apr-2019
SeRenA: a semantic recommender for all
Pages 558 - 562
Abstract
The growth of data available on the Web, especially through social networks and business transactions, has served as a driving force for the development of recommender systems. Although there are many techniques in the literature, these systems suffer from some problems, including the well-known cold start problem. This problem is related to the recommendations of new elements or new users when there is no initial knowledge base. In this study we propose a solution to this and other problems based on use of semantic. We present SeRenA (Semantic Recommender for All), an unsupervised recommending strategy based on extraction of initial interests through online data (eg. posted-message and friendship) and mapped onto a number of Wikipedia documents. We introduce the methods and techniques we plan to apply to discover new items over ambiguous knowledge base.
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Index Terms
- SeRenA: a semantic recommender for all
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Published In
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
- General Chairs:
- Sole Pera,
- Michael Ekstrand,
- Program Chairs:
- Xavier Amatriain,
- John O'Donovan
Copyright © 2018 Owner/Author.
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Publication History
Published: 27 September 2018
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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada
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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%
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View all- Hawashin BAlzubi SKanan TMansour A(2019)An efficient semantic recommender method forArabic textThe Electronic Library10.1108/EL-12-2018-024537:2(263-280)Online publication date: Apr-2019
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