Most recommendation systems do not take in different variables such as the persons mood, the time of day, location, or the weather. Most just use a system that figures out if a person will like something or not. For example, I like the movie Gladiator, but I do not want to watch it all the time. I have to be in the right mood in order to want to watch that movie. This paper explains how to create a content aware recommendation system.
Some of the most popular and best systems use collaborative filtering. This system uses ratings from a large set of users to predict if you would like it or not. However, it fails to take account of age, gender, and education. It mainly uses the similarity between genres, actors, and directors to see if people would like this movie. This paper goes into calculating the users location so it can be added in to the recommendation system. Users in the same geographical area generally have more similar tastes than someone halfway across the world. Most movie recommendation systems also do not take into account multiple users on one account. It is becoming more common that a family will have an iTunes or Netflix subscription and more than one person will watch a movie. They usually watch it for different reasons and more than likely they have different tastes. So the recommendations not apply to every user.
In conclusion, this paper talked about context aware movie recommendation systems. Machine learning is a way to improve the system. It also comes as no surprise that adding more input to the system will create a better system, such as location, time of day, etc. Even with these new data fields, it doesn't necessarily mean a better system. The difficult part will be how to integrate the new fields to create better recommendations.
This blog was written about the paper listed below:
Context-aware Movie Recommendation based on Signal
Processing and Machine Learning
http://dl.acm.org/citation.cfm?id=2096112.2096114
No comments:
Post a Comment