Abstract:
Recommender Systems are being widely used in many application
settings to suggest products, services, and items to potential users. They are the
software techniques providing suggestions for items to be of use to a user.
The main purpose of Recommender Systems is to generate meaningful
recommendations about the items to a collection of users for their interested
items. A variety of approaches in recommendation are user- based
collaborative filtering, item-based collaborative filtering, model- based
collaborative filtering, content-based recommendation, context- aware
recommendations and so on. However, there are two main approaches in
recommendation: user-based and item-based collaborative filtering and the
difference between them is that user-based takes the users’behavior and
item-based takes items’ rating values for similarity measurement. Since the
computational complexity of user-based recommendation grows linearly
with the number of users, item-based recommendation techniques have
been developed. The goal of this system is to provide meaningful
recommended applications to the mobile phone users that are relative to
their needs or targets. This system uses K- means clustering algorithm to
cluster the users based on their age and rating values and item-based
collaborative filtering method based on rating values of the items. By using
this system, the mobile phone users can get very effective recommendations
about applications without waste of time and effort.
