Browsing by Author "Wu, Bo"

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DRESSING FOR ATTENTION: OUTFIT BASED FASHION POPULARITY PREDICTION
(2019-01-01)
Lo, Ling
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Liu, Chia-Lin
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Lin, Rong-An
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Wu, Bo
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Shuai, Hong-Han
Accurate analysis of fashion trends is crucial. However, existing predictive algorithms of fashion popularity are restricted to be feasible on the coarse style level but not a finer item level. That is, they are only predictive in the future popularity of a given type of fashion styles (e.g., Rocker), but cannot be precisely down to a particular outfit look chosen by individuals. This paper thus proposes the first solution directly aimed at predicting the fine-grained fashion popularity of an outfit look by taking social media as the learning source. Particularly, a deep temporal sequence learning framework is developed and the proposed framework is evaluated on a real dataset of 380,000 street fashion images collected from the fashion website lookbook.nu. The experimental results show that our proposed framework outperforms the state-of-the-art approaches, with a relative increase of 11.51% to 27.62% (MSE metric) and 7.02% to 32.61% (CSE metric) in the prediction accuracy.
SMP Challenge: An Overview of Social Media Prediction Challenge 2019
(2019-01-01)
Wu, Bo
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Cheng, Wen-Huang
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Liu, Peiye
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Liu, Bei
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Zeng, Zhaoyang
"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic decisions for the future, but also explores advanced predictive learning and analytic methods on various problems and scenarios, such as multimedia recommendation, advertising system, fashion analysis etc. In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.) of new online posts in social media feeds before uploading. We also collected and released a large-scale SMPD benchmark with over 480K posts from 69K users. In this paper, we define the challenge problem, give an overview of the dataset, present statistics of rich information for data and annotation and design the accuracy and correlation evaluation metrics for temporal popularity prediction to the challenge.
Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters
(2020-03-01)
Wu, Bo
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Cheng, Wen-Huang
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Zhang, Yongdong
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Cao, Juan
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Li, Jintao
Retweeting is a powerful driving force in information propagation on microblogging sites. However, identifying the most effective retweeters of a message (called the "key retweeter prediction" problem) has become a significant research topic. Conventional approaches have addressed this topic from two main aspects: by analyzing either the personal attributes of microblogging users or the structures of user graph networks. However, according to sociological findings, author-retweeter dependencies also play a crucial role in influencing message propagation. In this paper, we propose a novel model to solve the key retweeter prediction problem by incorporating the auxiliary relations between a tweet author and potential retweeters. Without loss of generality, we formulate the relations from four relational factors: status relation, temporal relation, locational relation, and interactive relation. In addition, we propose a novel method, called "Relation-based Learning to Rank (RL2R)," to determine the key retweeters for a given tweet by ranking the potential retweeters in terms of their spreadability. The experimental results show that our method outperforms the state-of-the-art algorithms at top-k retweeter prediction, achieving a significant relative average improvement of 19.7-29.4 percent. These findings provide new insights for understanding user behaviors on social media for key retweeter prediction purposes.