Mining mood-specific movie similarity with matrix factorization for context-aware recommendation

     This paper uses a few different methods in order to recommend a movie to the user, but the one that stands out most is the mood specific option. The team treats all movies as similar weights even if the rating is low. They use factors such as time of day, and other options in order to rate the similarity between the mood of movies and try to improve the recommendation.
     This is a new model for the context aware recommendation system. They also propose using a matrix factorization model which is less computationally expensive than other methods. This allows them to compare a large data set quicker than others. This fact was learned during the Netflix Challenge. The research in this paper takes place during the Movieplot Challenge.
     The reason that they do not rely solely on user ratings is because only a few movies actually receive high ratings from a large set of users and those movies would be recommended all the time. They try to use multiple inputs in order to select a movie based on the mood and interests of a user. They go on to show the algorithms they used to compute the various data and along with the sudocode. They then evaluate the results using Mean Average Precision (MAP) and Area Under Curve (AUC).


The above blog was written about the following paper, it can only be accessed while on the Texas A&M campus:

Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
http://dl.acm.org/citation.cfm?id=1869658