A User-Centric Approach for Social Data Integration and Recommendation

Abstract

It is difficult for online users to keep track of their social friendships and friends' social activities scattered across different social networking sites. We propose a usercentric approach for integrating social data from different social networking sites and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different social networking sites. They can also rate friends and their activities as favourite, neutral or disliked. Our approach then provides personalized recommendations of friends' activities that may be interesting to each user. A prototype of a dashboard application called SocConnect (Social Connect) is also implemented to demonstrate the feasibility of our approach, along with evaluation on real users that confirms the appropriateness and effectiveness of our approach.

Combining Algorithms for Recommendation System on Twitter

“…The result of the study of CRSM, Twitter may have great effectiveness with the recommendation system, this can be seen by the percentage of correct classification prediction at 83.9 % and model goodness of fit at 54.3% and best performance of algorithm was interest from Twitter users at 23.86%. Future research, we should look at additional dimensions in the recommendation system cross different platforms not only Twitter but also Facebook and integrate data from both social networks [4,6].…”

“…Therefore, most recommendation systems using collaborative filtering are based on the sharing of user ratings. [2,6,7,9,12] Support Vector Machine (SVM). SVMs are useful technique for data classification by constructing an N-dimensional hyper plane that optimally separates the data into two categories and learning algorithms that can be analyzed theoretically using concepts from computational learning theories.…”

The “Wisdom of the Crowd Pattern”: A person-centric data aggregation approach for social software

“…Advantages of the data federation approach in contrast to the pipes and filters approach are outlined also in [9]. A dedicated person-centric integration approach is presented in [10] which is implemented in a desktop application. The solution aims to combine the profiles of a person from different social networking sites and creates an aggregated stream of friend activities from a single-user perspective.…”

Gradually adaptive recommendation based on semantic mapping of users′ interest correlations

“…Wang et al . presented a user‐centric method based on information sharing among similar users, which can integrate social data from different social networking sites and allow users to create personalized social and semantic contexts for their social data. Similarly, Q. Wang et al .…”