A Survey of Activity Based Friend Recommendation Services
نویسندگان
چکیده
In this paper, we have presented a literature review of the modern Activity based friend recommendation services. Social networking sites imply friend recommendation Systems in contribution to providing better user experiences. Online friend recommendation is a rapid developing topic in web mining. Current social networking servicing recommend friends to users based on their social graphs and mutual friends , which may not be the most appropriate to reflect a user’s taste on friend selection in real lifetime . In this paper propose a system that recommends friends based on the daily activities of users. Here a semantic based friend recommendation is done based on the users life styles. By using text mining, we display a user's everyday life as life archives, from which his/her ways of life are separated by using the Latent Dirichlet Allocation algorithm. At that point we discover a similarity metric to quantify the similarity of life styles between users, and ascertain users effect as far as ways of life with a similarity matching diagram. At last, we incorporates a feedback component to further enhance the proposal precision.
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