Abstract
Popularity prediction is important for many applications such as service design, network management and so on. Among several factors affecting popularity, content plays a key role, especially when we lack the time sequence data of historical consumption. However, exploring the influence of content-factors on popularity is not easy because of the increasing heterogeneous modalities and their sophisticated inner interplay. In this paper, we utilize several modes to predict popularity. In the meanwhile, considering that it is difficult and little significant to predict the exact number of popularity, we aim to rank pairs of content which is called relative popularity prediction. We cast the relative popularity prediction problem as a classification task and propose an end-to-end multi-modality model with the help of deep neural network. This model combines visual and textual information, maps them to a common feature space and implicitly constructs the interaction between them. Experimental result on real-world data has demonstrated the effectiveness of our model.
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Cai, H., Zhang, Y., Wang, Y., Wang, X., Mei, J., Huang, Z. (2018). Predicting Relative Popularity via an End-to-End Multi-modality Model. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_32
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DOI: https://doi.org/10.1007/978-981-10-8108-8_32
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