Web advertising recommender system based on estimating users' latent interests

Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services Pub Date : 2016-11-28 DOI:10.1145/3011141.3011180

Y. Yamaguchi, Mimpei Morishita, Y. Inagaki, Reyn Y. Nakamoto, Jianwei Zhang, Junichi Aoi, Shinsuke Nakajima

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引用次数: 8

Abstract

Web advertising is watched with interest as an advertising method employed by companies to introduce their products and services. Web advertising includes listing advertisement, which shows advertisements related to a search keyword, and interest-matching advertising, which shows advertisements relevant to a user's search content and browsing history. However, it is difficult to show effective Web advertising to potential purchasers using the technique based on conventional keyword matching. In this paper, we consider a recommender system for Web advertising based on analysis of the user's potential interests. In particular, we focus on a user model with potential interest for a certain website by analyzing browsing history. We introduce a Web advertising recommender system that is based not only based on keyword matching, but also on reported learning results. In addition, we argue the influence of the period for acquisition of the browsing history, which is taken when the users' model is learned.

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基于用户潜在兴趣估计的网络广告推荐系统

网络广告作为公司用来介绍其产品和服务的一种广告方式，受到人们的关注。网络广告包括列表广告和兴趣匹配广告，前者显示与搜索关键字相关的广告，后者显示与用户搜索内容和浏览历史相关的广告。然而，利用传统的关键词匹配技术很难向潜在购买者展示有效的网络广告。在本文中，我们考虑了一个基于用户潜在兴趣分析的网络广告推荐系统。特别是，我们通过分析浏览历史来关注对某个网站有潜在兴趣的用户模型。我们介绍了一个网络广告推荐系统，它不仅基于关键词匹配，而且基于报告学习结果。此外，我们还讨论了获取浏览历史记录的时间的影响，这是在学习用户模型时采取的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

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