Recommendations based on personalized tendency for different aspects of influences in social media
Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. To resolve this problem, most researches only utilize users' preferences, the content of items or social influence to make recommendations. However, people's preferences for items may be affected by social friends, personal interest and item popularity. Moreover, each factor has a different impact on each user. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these factors to recommend photos in a photo sharing website, Flickr. The personal tendencies related to these three influences are regarded as personalized weights to combine influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.
J. Inf. Sci.