User Data Analyticsand Recommender System for Discovery Engine

Abstract: On social bookmarking website, besides saving, organizing and sharing web pages,users canalso discovery new web pages by browsing other’s bookmarks. However, as more and more contents are added, it is hard for users to find interesting or related web pages or other users who share the same interests. In order to make bookmarks discoverable and build a discovery engine, sophisticated user data analytic methods and recommender system are needed. This thesis addresses the topic by designing and implementing a prototype of a recommender system for recommending users, links and linklists. Users and linklists recommendation is calculated by content-based method, which analyzes the tags cosine similarity. Links recommendation is calculated by linklist based collaborative filtering method. The recommender system contains offline and online subsystem. Offline subsystem calculates data statistics and provides recommendation candidates while online subsystem filters the candidates and returns to users. The experiments show that in social bookmark service like Whaam, tag based cosine similarity method improves the mean average precision by 45% compared to traditional collaborative method for user and linklist recommendation. For link recommendation, the linklist based collaborative filtering method increase the mean average precision by 39% compared to the user based collaborative filtering method.