Friends-and-native-people-aware approach for Collaborative Filtering
Aaron Ling Chi Yi, Dae-Ki Kang · 2014
In our daily lives, recommendation plays an important role for generating useful predictions. Recommender systems generate predictions for the users based on their preferences. Collaborative Filtering (CF) is one of the techniques that is widely used by many recommender systems. In order to make the recommendations, CF uses known preferences to generate personal recommendations that suit the user. In this paper, we propose a location-based recommender system called Friends-and-native-people-aware Approach for Collaborative Filtering. The main purpose of our recommender system is to consider only the opinions of friends and of people living in the place where the users wish to go. We only consider friends and native people opinions since friends have the common interests and preferences with the user while native people know good local places or activities to suggest to. By combining those two inputs, we believe that this method will produce better recommendations in terms of a better quality and personalized location recommendation.