Abstract
One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.
Highlights
The Internet environment has changed in recent years based on the development of wireless network technology and the spreading use of mobile devices
We apply the database to the recommendation systems based on genre correlations as content-based filtering [21]; the system takes genre combinations as inputs
We test the usability of the magic number by applying the magic number to a recommendation system based on user-based, item-based and content-based filtering; the recommendation system using user- or item-based filtering utilizes users’ ratings as its initial inputs, whereas using content-based filtering employs a genre
Summary
The Internet environment has changed in recent years based on the development of wireless network technology and the spreading use of mobile devices. On YouTube, users can upload their own video content, watch media content provided by other users, evaluate content and add comments. These user activities are all valuable in providing users with better service via user profiling [1] and content evaluations [2]. In recent years, both the content uploaded to the Internet by users and the content provided by conventional providers has steadily increased. This gives rise to a challenging problem: How can users effectively find what they really want from the vast available content?
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