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With the increase in volume of daily online news items, it is more and more difficult for readers to identify news articles relevant to their interests. Thus, effective recommendation systems are critical for an effective user news consumption experience. Existing news recommendation methods usually rely on the news click history to model user interest. However, there are other signals about user behaviors, such as user commenting activity, which have not been used before. We propose a recommendation algorithm that predicts articles a user may be interested in, given her historical sequential commenting behavior on news articles. We show that following this sequential user behavior the news recommendation problem falls into in the class of session-based recommendation. The techniques in this class seek to model users' sequential and temporal behaviors. While we seek to follow the general directions in this space, we face unique challenges specific to news in modeling temporal dynamics, e.g., users' interests shift over time, users comment irregularly on articles, and articles are perishable items with limited lifespans. We propose a recency-regularized neural attentive framework for session-based news recommendation. The proposed method is able to capture the temporal dynamics of both users and news articles, while maintaining interpretability. We design a lag-aware attention and a recency regularization to model the time effect of news articles and comments. We conduct extensive empirical studies on 3 real-world news datasets to demonstrate the effectiveness of our method. 

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that the early dynamics of user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health). 

Gallium‐based liquid metal (LM) composite with metallic fillers is an emerging class of thermal interface materials (TIMs), which are widely applied in electronics and power systems to improve their performance. In situ alloying between gallium and many metallic fillers like copper and silver, however, leads to a deteriorated composite stability. This paper presents an interfacial engineering approach using 3‐chloropropyltriethoxysilane (CPTES) to serve as effective thermal linkers and diffusion barriers at the copper‐gallium oxide interfaces in the LM matrix, achieving an enhancement in both thermal conductivity and stability of the composite. By mixing LM with copper particles modified by CPTES, a thermal conductivity (κ) as high as 65.9 W m⁻¹K⁻¹is achieved. In addition, κ can be tuned by altering the terminal groups of silane molecules, demonstrating the flexibility of this approach. The potential use of such composite as a TIM is also shown in the heat dissipation of a computer central processing unit. While most studies on LM‐based composites enhance the material performance via direct mixing of various fillers, this work provides a different approach to fabricate high‐performance LM‐based composites and may further advance their applications in various areas including thermal management systems, flexible electronics, consumer electronics, and biomedical systems.