Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
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Presentation at CASCON 2014, Toronto, ON, CA
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Moviesion: Content-based Movie Recommender Fueled by Linked Open Data
- 10. 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
- 11. 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 population Collection Genetic Algorithm In each iteration
- 12. CREW Assistant Professor EBRAHIM BAGHERI, EE. OF RYERSON UNIVERSITY JELENA JOVANOVIC, DEPT. OF SOFTWARE ENG., UNIVERSITY OF BELGRADE Research Associate JAVAD GHATTA Ph.D. Student ANDISHEH KEIKHA HOSSEIN FANI M.Sc. Student YUE FENG MAHTA MOATTARI
Editor's Notes
- Recommend Related Movies
- Search for related movies!
- Old school recommendation: Collaborative Filtering
- Jinni & similar techs
- Moviesion: Fully semantic-based recommendation Filtering based on genres Aggregates movie-information from any source within moments of its generation on the Web Fully links its structured movie-data Semantic plot-based similarity Content-based recommendation Uses semantic annotation (Denote) Cover non-mainstream movies in recommendations
- When it works! Let me recommend you some movies! Which Movie do you like? Silence of the lambs This might be your favourite list: Hanibal, Red Dragon, … Hannibal (film) [0.74] Hannibal Rising (film) [0.59] Red Dragon (film) [0.47] Battling with Buffalo Bill [0.26] The White Buffalo [0.22] Buffalo Bill (film) [0.18] Pootie Tang [0.17] Eyes Wide Shut [0.17] Firewall (film) [0.15]