Movie Recommendation Task

A Movie Recommendation Task is an entertainment item recommendation task that requires a movie item to be recommended to an entertainment consumer.

• Context:
  • It can range from being a Heuristic Movie Recommendation Task to being a Data-Driven Movie Recommendation Task.
  • It can be solved by a Movie Recommendation System (that implements a movie recommendation algorithm).
• Example(s):
  • a Netflix Prize Task.
  • based on movie ratings data from http://grouplens.org/datasets/movielens/
• Counter-Example(s):
  • http://dextra.sg/challenges/rakuten-viki-video-challenge/ requires participants to predict for each user a set of TV drama episodes that users would watch with interest.
  • a Music Recommendation Task.
  • a Video Game Recommendation Task.
  • a Credit Scoring Task.
• See: Recommender System, Movie Rating, Practical Task.

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References

2016

• http://mapr.com/products/mapr-sandbox-hadoop/tutorials/recommender-tutorial/
  • QUOTE: This tutorial will give step-by-step instructions on how to:
    • Use sample movie ratings data from http://grouplens.org/datasets/movielens/
    • Use a collaborative filtering algorithm from Apache Mahout to build and train a machine learning model
    • Use the search technology from Elasticsearch to simplify deployment of the recommender

2002

• (Schein et al., 2002) ⇒ Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. (2002). “Methods and Metrics for Cold-Start Recommendations.” In: Proceedings of the 25th ACM SIGIR Conference (SIGIR 2002) doi:10.1145/564376.564421.
  • Figure 1 (a) shows a graphical model description of the aspect model for a person/movie contingency table and Table 1 explains our notation used in the graphical model as well as in other descriptions of the movie recommendation task.

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