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Elastic recommendation (Thesis projects) Slide 1 Elastic recommendation (Thesis projects) Slide 2 Elastic recommendation (Thesis projects) Slide 3 Elastic recommendation (Thesis projects) Slide 4 Elastic recommendation (Thesis projects) Slide 5 Elastic recommendation (Thesis projects) Slide 6 Elastic recommendation (Thesis projects) Slide 7
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DITAS Project (Alexandros Psychas)

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Elastic recommendation (Thesis projects)

  1. 1. ELASTIC RECOMMENDATION Alexandros Psychas
  2. 2. MOTIVATION • Elastic search is a very powerful cutting-edge search engine • Developed for text analysis • The capabilities of elastic search are enormous • used in many research publications • Linked in profiles[1] • Job Offerings[2] • Medical Data[3] • Although elasticSearch is very powerful and highly configurable, it is created mainly for text-based search and evaluation
  3. 3. FUTURE • Vectorization for elasticSearch • Combination with vector-based algorithm(K-NN) for image correlation • Combination with convolutional NN for image retrieval
  4. 4. OWN APPROACH
  5. 5. USER RELEVANCE Current user Requirements Elastic Search for vectorize stored user Requirements Use K-NN to Find User Requirement Relevance Produce Mean User Relevance
  6. 6. FUNCTION FOR PRODUCING SCORE • F(Blueprint)=C +U+S+P+BR • C: content based score • U: Data utility score • S: Security Score • P: Price • BR: (Reputation, Popularity)*Use Relevance • Reputation is the average User score of the users that used this blueprint • Popularity is the number of users used this Blueprint • User Relevance is a factor derived from the relevance of the current user requirements and the user requirements of the users that have purchased the Blueprint (Sum of User Relevance/ number of users)
  7. 7. THANK YOU FOR YOUR ATTENTION
  • ShelonKirkpatrick

    Nov. 30, 2021

DITAS Project (Alexandros Psychas)

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