  As one of the most popular services over online communities, the social
recommendation has attracted increasing research efforts recently. Among all
the recommendation tasks, an important one is social item recommendation over
high speed social media streams. Existing streaming 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 streaming
environments. Specifically, we first propose a novel Bi-Layer Hidden Markov
Model (BiHMM) that adaptively captures the behaviors of social users 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 effectively identifying the relevance score of a streaming item to a
user. Following that, we propose a novel indexing scheme called {\Tree} for
improving the efficiency of our solution. Extensive experiments are conducted
to prove the high performance of our approach in terms of the recommendation
quality and time cost.
