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Page 1: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
Page 2: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
Page 3: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data

04/15/2023 page 3

Page 4: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
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Page 7: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
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Page 9: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data

Freebase

WikipediaDbpedia

page 9

Pool of all

Movies

Structured

Crawled and processed

Structured

Page 10: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data

page 10

Genetic Algorithm Generating Output

Plot Semantic Similarity

Calculation

Update Top K recommendations

Genre Filtering

Sorting

Syntactical Similarity

Calculation

Top k Recommended

Movies

Collecting Initial Population

Page 11: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data

11

Fitness Calculation

Plot semantic Similarity

DISCO similarity measure

Crossover, Mutation

Binary representation

One-point crossover

Selection

Roulette wheel

selection

Keep the best ones

Update Top K Recommendation

Keep the best for the top K

Check the stopping criteria

Process Overview

Initial popu-lation Col-lection

Genetic Algorithm In each

iteration

Page 12: Moviesion: Content-based Movie Recommender Fueled by Linked Open Data

CREW

Assistant Professor EBRAHIM BAGHERI, EE. OF RYERSON UNIVERSITYJELENA JOVANOVIC, DEPT. OF SOFTWARE ENG., UNIVERSITY OF BELGRADE

Research Associate JAVAD GHATTA

Ph.D. Student ANDISHEH KEIKHAHOSSEIN FANI

M.Sc. Student YUE FENGMAHTA MOATTARI


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