'Fitness that Fits': - A Prototype Model for Workout Video Recommendation

Ercan Ezin, Eunchong Kim, Ivan Palomares Carrascosa

Research output: Contribution to conferenceConference Paperpeer-review

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Personalization services enable Internet users to benefit from tailored recommended content at their finger tips. Our interest in
this contribution lies in video recommendation within the fitness
domain to support an active lifestyle. We present ’Fitness that Fits’, a preliminary platform for workout video recommendation, which benefits from the Youtube-8M labeled dataset and its rich variety of categorized video labels, thereby enabling fitness workout video recommendations predicated on the users’ preferences and their recent viewing behavior. The proposed model also incorporates an approach to produce diversified recommendations and foster the practice of distinct fitness activities based on like-minded users’ information. An initial experimental study shows the trade-offs of the hybrid approach considered.
Original languageEnglish
Number of pages6
Publication statusPublished - 2 Oct 2018
Event12th ACM Conference on Recommender Systems - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018


Conference12th ACM Conference on Recommender Systems
Abbreviated titleRECSYS 2018
Internet address

Structured keywords

  • Jean Golding


  • Personalized Wellbeing
  • Preference Modeling
  • Diversity


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