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.