movie recommender system
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GROUP 2:
Vindhya Alahari 0004/48Anand Raj 0041/48Hara Prasanna Naik 0138/48
Akshat Jaiswal 0157/48Namrata Penta 0258/48
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System overview 3 modules
1. Recommend genre
2. Recommend Movie by genre and User Rating3. Personalised Recommendation based on User
characteristics
Source of data (Knowledge) : Movie Lens website
http://www.grouplens.org/node/12
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Module 1-Recommend genre Inputs-
Age
Gender Output (Recommendation)
Genre
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Recommend Movie by genre Inputs-
Genre
User rating (Top 5 Movies) Output (Recommendation)
Movie
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Personalised Recommendation Input User characteristics-
Age
Gender Occupation
Output (Recommendation)
Movie
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Expert system components Knowledge-
Heuristics created from the Movie lens website data Rules generated by running association mining and k-means
clustering on the data using clementine software
Inference engine 8 variables, 4 Logic blocks and 4 command blocks in the
CORVID software Run the generated rules using the heuristic knowledge and
the user input data
User Interface Display prompt of the corvid package Can be customised further to display on a browser or generate
a report
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Further Applications Including more diverse user characteristics like region,
language
Recommending songs based on mood Recommendation based on past user preferences
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