Session-based News Recommendation from Temporal User Commenting Dynamics
Session-based News Recommendation from Temporal User Commenting Dynamics
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DOI：
10.1109/asonam55673.2022.10068595
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Chen Shen;Chao Han;Lihong He;Arjun Mukherjee;Z. Obradovic;E. Dragut
中科院分区：
文献类型：
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作者：
Chen Shen;Chao Han;Lihong He;Arjun Mukherjee;Z. Obradovic;E. Dragut
随着每日网络新闻量的增加，读者越来越难以识别与其兴趣相关的新闻文章。因此，有效的推荐系统对于有效的用户新闻消费体验至关重要。现有的新闻推荐方法通常依赖于新闻点击历史来建模用户兴趣。然而，还有其他关于用户行为的信号，例如用户评论活动，这些信号以前没有使用过。我们提出了一个推荐算法，预测用户可能感兴趣的文章，她的历史顺序评论行为的新闻文章。我们表明，这种顺序的用户行为的新闻推荐问题福尔斯属于类基于会话的建议。本课程中的技术试图对用户的顺序和时间行为进行建模。虽然我们寻求遵循这个领域的总体方向，但我们在建模时间动态方面面临着特定于新闻的独特挑战，例如，用户的兴趣随着时间而改变，用户对文章的评论不规则，并且文章是具有有限寿命的易变质物品。我们提出了一个最近规则化的神经注意框架，用于基于会话的新闻推荐。所提出的方法是能够捕捉用户和新闻文章的时间动态，同时保持可解释性。我们设计了一个具有滞后意识的注意力和新近度正则化模型来模拟新闻文章和评论的时间效应。我们进行了广泛的实证研究3真实世界的新闻数据集，以证明我们的方法的有效性。
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.