Social networks have become an important information source. Due to
their unprecedented success, these systems have to face an exponentially
increasing amount of user generated content. As a consequence, finding
relevant users or data matching specific interests is a challenging. We
present RecLand, a recommender system that takes advantage of the social
graph topology and of the existing contextual information to recommend
users. The graphical interface of RecLand shows recommendations that
match the topical interests of users and allows to tune the parameters
to adapt the recommendations to their needs.
Personality Based Recommender Systems are the next generation of recommender systems because they perform far better than Behavioural ones (past actions and pattern of personal preferences)
Twitter teams with IBM for business analytics
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
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