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A movie recommender system that takes user relevance feedback to improve the recommendation. The system is developed using SVD matrix factorization and optimized using ALS method

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jadhavhninad/Latent-Semantics-Based-Movie-Recommender-System

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Task 1:

Implement a program which given all the information available about the sequence of movies a given user has watched, recommends the user 5 more movies to watch, using SVD.

The order in which the movies are watched and the recency shoudl also be taken into account.

The result interface should also allow the user to provide positive and/or negative feedback for the ranked results returned by the system to enable Task 2.

Task 2:

Relevance feedback task (content): Implement a probabilistic relevance feedback system to improve the accuracy of the matches from Tasks 1a through 1e. The system should also output the revisions it suggests. See Gerard Salton and Chris Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science. 41, pp. 288-297, 1990.

User feedback is than taken into account (either by revising the query or by re-ordering the results as appropriate) and a new set of ranked results are returned.

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A movie recommender system that takes user relevance feedback to improve the recommendation. The system is developed using SVD matrix factorization and optimized using ALS method

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