Predicting the popularity of web 2.0 items based on user comments

By Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama. 2014

112 citations recorded by the source corpus.

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets --- crawled from YouTube, Flickr and Last.fm --- show that our method consistently outperforms competitive baselines in several evaluation tasks.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Neural Collaborative Filtering (2017, background): “While a non-uniform sampling strategy ( e.g., item popularity-biased [14, 12]) might further improve the performance, we leave the exploration as a future work.”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, background): “Recent work has highlighted the importance of incorporating temporality not only for fake news detection [26, 35], but also for modeling online information dissemination [14].”
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, unknown): “Copyright 2014 ACM 978-1-4503-2257-7/14/07 ...$15.00. http://dx.doi.org/10.1145/2600428.2609579.”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “, 2018b,a), or subsequent comment volume/popularity/structure (Szabo and Huberman, 2010; Kim et al., 2011; Tatar et al., 2011; Backstrom et al., 2013; He et al., 2014; Zhang et al., 2018b), or eventual post article score (Rangwala and Jamali, 2010; Szabo and Huberman, 2010),; but all where, like us,”
DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation (2018, background): “Recent works [ Pan et al. , 2008; Pan and Scholz, 2009; He et al. , 2014; 2016; Volkovs and Yu, 2015] has proposed non-uniform weighting strategies and sampling schemes to carefully select negative feedback from unobserved interactions.”
Neural Factorization Machines for Sparse Predictive Analytics (2017, background): “While this work endows FM with non-linearities from predictive model perspective, another viable solution for incorporating non-linearities is to extend the objective function with regularizers like the graph Laplacian [15, 34].”