Mehdi Elahi

ORCID iD
https://orcid.org/0000-0003-2203-9195
  • Country
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Italy

Sources:
Mehdi Elahi (2017-09-04)

  • Keywords
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Recommender Systems,

Sources:
Mehdi Elahi (2015-11-12)

Active Learning,

Sources:
Mehdi Elahi (2015-11-12)

Machine Learning,

Sources:
Mehdi Elahi (2015-11-12)

Artificial Intelligence,

Sources:
Mehdi Elahi (2015-11-12)

Data Mining,

Sources:
Mehdi Elahi (2015-11-12)

Data Science,

Sources:
Mehdi Elahi (2015-11-12)

Context awareness,

Sources:
Mehdi Elahi (2015-11-12)

Personality,

Sources:
Mehdi Elahi (2015-11-12)

Personalization,

Sources:
Mehdi Elahi (2015-11-12)

Philosophy,

Sources:
Mehdi Elahi (2015-11-12)

Psychology,

Sources:
Mehdi Elahi (2015-11-12)

Information retrieval,

Sources:
Mehdi Elahi (2015-11-12)

Human Computer Interaction,

Sources:
Mehdi Elahi (2015-11-12)

Software Engineering,

Sources:
Mehdi Elahi (2015-11-12)

Natural Language Processing,

Sources:
Mehdi Elahi (2015-11-12)

Collaborative Filtering,

Sources:
Mehdi Elahi (2015-11-12)

Social Network Analysis,

Sources:
Mehdi Elahi (2015-11-12)

Signal Processing,

Sources:
Mehdi Elahi (2015-11-12)

Video Processing,

Sources:
Mehdi Elahi (2015-11-12)

Tourism,

Sources:
Mehdi Elahi (2015-11-12)

Big Data,

Sources:
Mehdi Elahi (2015-11-12)

Decision Making

Sources:
Mehdi Elahi (2015-11-12)

  • Websites
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@DBLP

Sources:
Mehdi Elahi (2016-02-12)

@ACM

Sources:
Mehdi Elahi (2016-02-12)

@Research Gate

Sources:
Mehdi Elahi (2016-02-12)

@Google Scholar

Sources:
Mehdi Elahi (2016-02-12)

@Linkedin

Sources:
Mehdi Elahi (2016-02-12)

@Academia

Sources:
Mehdi Elahi (2016-09-06)

@Slideshare

Sources:
Mehdi Elahi (2016-09-06)

  • Other IDs
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Scopus Author ID: 34879649100

Sources:
Scopus to ORCID (2015-11-12)

ResearcherID: O-4221-2015

Sources:
Clarivate Analytics (2015-11-23)

Biography

Mehdi Elahi received M.Sc. degree in Electrical Engineering (Sweden), and Ph.D. degree in Computer Science (Italy), under the supervision of Prof. Francesco Ricci. During the course of Ph.D., he has researched on Recommender Systems (RSs), mainly focused on the cold start problem. He has designed, developed, and evaluated (offline/online) several personalized techniques for Active Learning in RSs. These techniques were integrated in a mobile context-aware recommender system for tourism, called "South Tyrol Suggests". As a result of his graduate research work, Mehdi Elahi served as a primary author or co-author on several publications in AI, ML, RS, IR, HCI, and UM related conferences and journals. He was also given the opportunity to publish his research findings in a chapter of the Recommender Systems Handbook (2nd edition).
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