Advertising Slogan Generation System Reflecting Preferences of Users on the Web

Abstract

Increased demand for web advertising has resulted in a corresponding increase in the need to develop online personalized advertisement. This paper proposes an advertising slogan generation system reflecting preferences of users on the web. By using a social networking service (SNS) site as knowledge base of word preferences, and by employing an advertising slogan corpus, the proposed system aims to generate slogans that reflect advertising posts on SNS. Using model slogans selected from the corpus containing 24,472 slogans, the proposed system generates slogan candidates using the knowledge obtained from a post on SNS. These slogan candidates are selected by the following three indexes; the natural level given by a large scale balanced corpus, the semantic relations score using the advertising slogans, and the preference level obtained from SNS sites. Especially, the proposed system extracts preference data from these SNS fan pages and estimates preference level on each word based on bag-of-words model. This enables the proposed system to select slogans in fashion. The authors conducted an objective experiment to examine the quality of generated slogans. The result shows that (1) the natural level and semantic relation level are effective to select slogans that reflect a post (2) the preference level index contributes to select preferred slogans that interest people.