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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2133||2011||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 3, March 2011, Pages 1777–1788
Web advertising (online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the most commonly-used marketing channels. This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers’ interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog-sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers’ immediate personal interests in order to improve online contextual advertising. The proposed Blogger-Centric Contextual Advertising (BCCA) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger’s personal interests.
Blogosphere is a collective term comprising all blogs and their interconnections. A blog, short for weblog, is a type of web site that is usually maintained by a blogger who will publish serial journal posts containing news, comments, opinions, diaries, and interesting articles. As of December 2007, the blog search engine Technorati1 was tracking more than 112 million blogs. Reports also indicate that about 1.2 million new blogs are being created worldwide each day. According to Technorati’s reports in April 2007, the number of blogs in the top 100 most popular sites has risen substantially. Hence, blogs continue to become more and more viable news and information outlets. Blogs are also an increasingly attractive platform for advertisers. The majority of bloggers have advertising on their blogs. Marketers realize that bloggers are creating high-quality content and attracting growing and loyal audiences (Technorati, 2008). Hence, it is common for blogs to feature advertisements that either financially benefit the blogger or promote the blogger’s favorite causes. Bloggers can be classified into three types (Technorati, 2008). Personal bloggers blog about topics on personal interests not associated with their work, professional bloggers mainly blog about their industries and professions but not in an official capacity for their companies; and corporate bloggers usually blog for their companies in an official capacity. Statistics show that four out of five bloggers (about 79%) are personal bloggers. The majority of bloggers have advertising or another method of revenue generation on their blogs. Among bloggers who have advertising on their blogs, two out of three have contextual ads and one-third have affiliate advertising on their blogs (Technorati, 2008). On average, professional and corporate bloggers are more likely to include search ads, display ads and affiliate marketing, because they certainly understand what kinds of ads are suitable for their blogs. However, the majority of personal bloggers who have no specific idea which ads are proper to their web sites reply on reliable matching mechanisms used in contextual advertising. Hence, in this paper we hope to propose a contextual advertising mechanism that could increase click rates on personal blogs.
نتیجه گیری انگلیسی
In this study, we proposed and evaluated a novel framework for associating ads with blog pages based on intention analysis. Prior work to date has only examined the extent of content relevance between pages and ads. In this paper, we investigated the intentions and the sentiments of blog pages and utilized this information to demonstrate Blogger-Centric Contextual Advertising. For intention recognition, we adopted the information from ebay.com and Wikipedia as training data to recognize the latent immediate interest (intention-level). The results indicated that using SVM with features selected by chi-square method can yield average 95.0% and 83.2% F-measures for non-intentional and intentional class respectively. As for sentiment detection, we proposed a new method to label the sentences from epinions.com for training data preparation. Our results showed that using SVM with features selected by chi-square method can improve F-measure from 55% to 85% for subjective sentences, and 56.6% to 86% for positive sentences. As for page-ad matching, we evaluated our framework using 200 personal blog pages and over 130,000 ads sampled from Google AdSense. First, we compared BCCA with Google AdSense and found that our proposed method with personalized advertising mechanism can achieve superior performance (57% accuracy). We evaluated three well-known IR retrieval models. (i.e., Language Model, tf * idf, and Okapi BM25). Our results indicated that all three IR approaches can assign relevant ads to the personal interest aspects of a blog page. In general, language model has a better performance than tf * idf and Okapi BM25. In the future, we intend to conduct a more comprehensive analysis of our model and explore the effectiveness of intention recognition and sentiment detection using different machine learning algorithms (such as CRF) with different data sources (e.g., alatest.com and reviews.cnet.com) and natural language processing techniques (such as name entity). In this paper, we manually constructed the user profile for each blog web site. However, many users tend to give incorrect data for protecting privacy. We may also apply text classification techniques with the existing posts of blog to reason the user profile. In addition, we wish to apply the concept of topic analysis to pages and ads to enhance their performance.