社交媒体驱动的新闻个性化

S. O’Banion, L. Birnbaum, K. Hammond
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引用次数: 22

摘要

虽然社交媒体作为分享信息的平台已经取得了显著和广泛的应用,但它们作为预测用户兴趣的数据来源的用途尚未得到充分的探索。在本文中,我们提出了一种基于Twitter的基于内容的用户兴趣建模方法。我们的推荐系统使用信息检索技术将tweet和用户表示为新闻主题的集合,包括高级类别(例如,体育、政治、商业)和详细的子主题(例如,芝加哥公牛队、米特·罗姆尼、企业家精神)。我们讨论了一个系统的设计,该系统使用这些信息以个性化报纸的形式提供新闻推荐。最后,我们描述了一种评估基于Twitter的推荐系统的新方法,该方法涉及挖掘Twitter数据以识别新闻兴趣的明确指标,并将这些指标与追溯的系统推荐进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social media-driven news personalization
While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation systems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.
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