Embedding based recommender systems : a review and comparison
Other Title(s)
أنظمة الترشيخ باستخدام طبقات التضمين
Joint Authors
al-Kafrawi, Passent Muhammad
Rajab, Ahmad Husayn
Source
The Egyptian Journal of Language Engineering
Issue
Vol. 9, Issue 1 (30 Apr. 2022), pp.1-11, 11 p.
Publisher
Egyptian Society of Language Engineering
Publication Date
2022-04-30
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper provides a summary and review of embedding based recommender systems.
Word embedding frameworks like word2vec were originally developed for NLP tasks.
However, they were quickly adopted in recommender systems to construct hybrid recommenders that incorporate side information in addition to user-item interaction to overcome common problems in recommender systems like cold start and popularity bias.
However, there are several proposed recommender systems that utilize embedding layers and each of them has its own strengths and weaknesses.
A review and comparison between these different approaches is presented in this work.
First, normal word embedding for NLP is introduced then different recommenders that utilize this method are presented and compared.
Different evaluation metrics and standard datasets used for embedding based recommender systems are discussed afterwards and finally a unified comparison of all these datasets and evaluation metrics is presented in order to facilitate comparison between different embedding-based recommenders.
Future work is then presented and discussed.
American Psychological Association (APA)
Rajab, Ahmad Husayn& al-Kafrawi, Passent Muhammad. 2022. Embedding based recommender systems : a review and comparison. The Egyptian Journal of Language Engineering،Vol. 9, no. 1, pp.1-11.
https://search.emarefa.net/detail/BIM-1393804
Modern Language Association (MLA)
Rajab, Ahmad Husayn& al-Kafrawi, Passent Muhammad. Embedding based recommender systems : a review and comparison. The Egyptian Journal of Language Engineering Vol. 9, no. 1 (2022), pp.1-11.
https://search.emarefa.net/detail/BIM-1393804
American Medical Association (AMA)
Rajab, Ahmad Husayn& al-Kafrawi, Passent Muhammad. Embedding based recommender systems : a review and comparison. The Egyptian Journal of Language Engineering. 2022. Vol. 9, no. 1, pp.1-11.
https://search.emarefa.net/detail/BIM-1393804
Data Type
Journal Articles
Language
English
Notes
-
Record ID
BIM-1393804