A maximum entropy approach to natural language processing
Computational Linguistics
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
The influence of caption features on clickthrough patterns in web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query rewriting using active learning for sponsored search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Keyword generation for search engine advertising using semantic similarity between terms
Proceedings of the ninth international conference on Electronic commerce
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Optimizing relevance and revenue in ad search: a query substitution approach
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Data-driven text features for sponsored search click prediction
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
Advertiser-centric approach to understand user click behavior in sponsored search
Proceedings of the 20th ACM international conference on Information and knowledge management
Position-normalized click prediction in search advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Precise click prediction is one of the key components in the sponsored search system. Previous studies usually took advantage of two major kinds of information for click prediction, i.e., relevance information representing the similarity between ads and queries and historical click-through information representing users' previous preferences on the ads. These existing works mainly focused on interpreting ad clicks in terms of what users seek (i.e., relevance information) and how users choose to click (historically clicked-through information). However, few of them attempted to understand why users click the ads. In this paper, we aim at answering this ``why'' question. In our opinion, users click those ads that can convince them to take further actions, and the critical factor is if those ads can trigger users' desires in their hearts. Our data analysis on a commercial search engine reveals that specific text patterns, e.g., ``official site'', ``$x\%$ off'', and ``guaranteed return in $x$ days'', are very effective in triggering users' desires, and therefore lead to significant differences in terms of click-through rate (CTR). These observations motivate us to systematically model user psychological desire in order for a precise prediction on ad clicks. To this end, we propose modeling user psychological desire in sponsored search according to Maslow's desire theory, which categorizes psychological desire into five levels and each one is represented by a set of textual patterns automatically mined from ad texts. We then construct novel features for both ads and users based on our definition on psychological desire and incorporate them into the learning framework of click prediction. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that this approach can result in significant improvement in terms of click prediction accuracy, for both the ads with rich historical data and those with rare one. Further analysis reveals that specific pattern combinations are especially effective in driving click-through rates, which provides a good guideline for advertisers to improve their ad textual descriptions.