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Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users' interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users' current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users' current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t). Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.

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