Online Social Media Recommendation over Streams
Xiangmin Zhou, Dong Qin, Xiaolu Lu, Lei Chen, Yanchun Zhang

TL;DR
This paper introduces a new framework for social media recommendation over high-speed streams, utilizing a Bi-Layer Hidden Markov Model and innovative indexing to improve accuracy and efficiency.
Contribution
The paper presents a novel Bi-Layer Hidden Markov Model and a new indexing scheme for efficient, accurate social media recommendations over streaming data.
Findings
High recommendation quality demonstrated in experiments
Significant reduction in time cost achieved
Effective handling of diverse user interests
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Caching and Content Delivery · Advanced Image and Video Retrieval Techniques
