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October 30, 2020 04:28
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Time Commitment: 50 hours, Nov 23- Nov 30 ‘2018 | |
Create baseline model with python: | |
* Graph theory with python | |
* Poster representation | |
* Search-Even Misspelled words | |
* Multiple movies attributes- Persona and Word Cloud | |
* Filter of Genre, IMDB rating, Popularity and actor | |
* Comments and Screenshot capability- Login | |
* Hosting with a Virtual Machine | |
Helpful materials: | |
Hosting on Shiny: | |
https://daroczig.shinyapps.io/rinfinance_Berlinger-Daroczi-demo/ | |
https://www.kaggle.com/philippsp/book-recommender-collaborative-filtering-shiny | |
https://medium.com/neo4j/running-neo4j-on-google-cloud-6592c1b4e4e5 | |
https://rpubs.com/jeknov/movieRec | |
Hosting on Neo4j: | |
https://www.analyticsvidhya.com/blog/2018/04/introduction-to-graph-theory-network-analysis-python-codes/ | |
https://medium.com/elements/diving-into-graphql-and-neo4j-with-python-244ec39ddd94 | |
https://www.youtube.com/watch?v=c9z_ICD44XQ | |
https://vasturiano.github.io/3d-force-graph/example/large-graph/ | |
https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-recommendation-engine-python/ | |
https://larrydag.shinyapps.io/boardgame_reco/- Very Nice, Implement this with separate reccos and final | |
https://www.youtube.com/watch?v=bdQ90y9Pefo&_ga=2.127414349.1156660759.1542907416-1912202818.1538049138- R+Neo4J- Exactly what I have been looking for | |
https://neo4j.com/sandbox-v2/- Very nice+ Shiny(Finish by 5:00 pm) | |
https://muffynomster.wordpress.com/2015/06/07/building-a-movie-recommendation-engine-with-r/ | |
On selecting 10 movies, give the characteristic of persona: | |
Current process: Cosine similarity of movies, Pre-Train for Individual movies, 2 platforms- Tableau and Neo4j | |
Next Steps: | |
* Find method for on the fly calculations for selected items | |
* Get hold of More movies from IMDB(Current-Only 1700 for Hindi) | |
* Poster for more movies and interface for reccos with storyline and IMDB link |
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