Chen Chen
Hongzhi Yin
Abstract
As social media further integrates into our daily lives, people are increasingly immersed in real-time social streams via services such as Twitter and Weibo. One important observation in these online social platforms is that users' interests and the popularity of topics shift very fast, which poses great challenges on existing recommender systems to provide the right topics at the right time. In this paper, we extend the online ranking technique and propose a temporal recommender system - TeRec. In TeRec, when posting tweets, users can get recommendations of topics (hashtags) according to their real-time interests, they can also generate fast feedbacks according to the recommendations. TeRec provides the browser-based client interface which enables the users to access the real time topic recommendations, and the server side processes and stores the real-time stream data. The experimental study demonstrates the superiority of TeRec in terms of temporal recommendation accuracy.
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