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Recommendation Algorithm
Song Yikun edited this page Apr 13, 2017
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Our recommendation algorithm comprises of three steps:
- Seeding: select favourite movies as seeds for recommendation
- Associating: find similar movies based on the seeds
- Fitting: select based the projected score based on user history and public ratings
The latter two steps are very time consuming and take up most of the time of the recommending process for each user. Thus, the process must be optimised.
During the seeding step, we select all the movies that are rated above 8 points by user. These movies are considered user's favourite movies, and we use these movies as the starting point to generate a pool of similar movies for further selection.
To create a pool of similar movies,
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2.1 Team Details
2.2 Project Worklog
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5.1 Usability
5.2 Efficiency
5.3 Robustness
5.4 Security