Online Social Media Recommendation Over Streams

As one of the most popular services over online platforms, social recommendation has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is item recommendation over high speed social media streams. Existing stream recommendation techniques are not effective for handling social users with diverse interests. Meanwhile, approaches for recommending items to a particular user are not efficient when applied to a huge number of users over high speed streams. In this paper, we propose a novel framework for the social recommendation over streams. Specifically, we first propose a novel Bi-Layer Hidden Markov Model (BiHMM) that adaptively captures the users' behaviors and their interactions with influential official accounts to predict their long-term and short-term interests. Then, we design a new probabilistic entity matching scheme for identifying the relevance score of a streaming item to a user. Moreover, we propose a novel index scheme called CPPse-index for improving the efficiency of our solution. Extensive tests are conducted to prove the superiority of our approach in terms of the recommendation quality and time cost.