網絡用戶通常在提交一個查詢詞之前，會有一個特定的搜索目標。然而，許多非專業的使用者不能正確地將他們的搜索目標轉化為適合的查詢詞。因此，了解查詢詞背後的搜索目標是改善搜索引擎非常重要的議題。在過去幾年，許多研究專注於將搜索目標加以分類，包括信息類，導覽類和交易類。然而，一個查詢詞可能會有一個以上的搜索目標，以至於只是粗略的將搜索目標分成三類是不足夠的，因此需要有更好的方法來具體的描述使用者查詢詞確切的搜索目標。
　　近年來，互聯網用戶經常依靠搜索引擎來處理日常生活的各種任務，如婚禮策劃、旅遊規劃或求職。然而，傳統的搜索引擎往往將查詢詞視為可以由一個搜索目標來完成的簡單搜索任務。事實上，越來越多的查詢詞是基於複雜的任務，其中包含一個以上的子搜索任務，從而迫使用戶發出一連串的查詢。例如，要完成一個複雜的任務“準備婚禮”必須滿足多個子任務，如“買婚戒”，“準備婚禮場地”和“租用豪華轎車”。因此，要完成這一複雜的任務，用戶至少需要提交三個查詢詞。
　　此外，我們觀察到，一個複雜的任務中的每個子任務可能觸發不同的消費需求。為了滿足各個子任務的消費需求，當一般的網頁搜尋結果未能滿足他們的消費需求時，用戶可能傾向點擊網頁搜索結果的贊助商廣告。近年來研究指出，有消費需求的用戶時在網上搜索時更有可能點擊廣告（相較於一般的網頁搜尋結果）。這表示理想的推薦廣告可以改善用戶的搜索體驗。然而，給定一個複雜的任務的查詢，傳統的搜索引擎通常不能夠提供合適的廣告以滿足所有從子任務衍生出的消費需求。
　　為了提高廣告的效益，廣告推薦系統應優化建議廣告的消費率。然而、最近的一些研究只是集中在增加廣告的點擊率。提高廣告點擊率並不能準確反映該廣告是否真正觸發用戶內心的消費需求，而且可能不會增加消費率。事實上，一些用戶可能基於好奇心來點擊廣告，卻不會在被廣告的網站作任何的消費行為。因此，這表示有吸引力的廣告，應該要真正滿足用戶內心的消費需求，也會提高消費率和降低用戶的搜索時間。此外，廣告商也會獲得較大的利潤並因此創造一個用戶和廣告商雙贏的局面。就我們所知，我們是第一個處理針對複雜任務提供廣告的問題。
為了幫助用戶更快完成他們的複雜搜尋任務，在本論文中，我們首先提出了一個基於查詢詞自動偵測用戶搜尋目的模型。接著，我們進一步建構複雜搜尋任務的結構，其結構包含複雜搜尋任務名稱和子搜尋任務。最後，我們將所建構的複雜搜尋任務，應用在廣告搜尋上，這種廣告模式可以同時推薦不同子任務的廣告，一次滿足用戶處理複雜任務所有可能的需求。我們在本研究中開發了一套雛型系統，希望可以幫助用戶有效的完成他們的複雜搜尋任務。

Web users usually have a certain search goal before they submit a search query. However, many laypersons can’t transform their search goals into suitable queries. Thus, understanding original search goals behind a query is very important for search engines. In the past decade, many researches focus on classifying search goals behind a query into different search-goal categories, including informational, navigational, and transactional. In fact, there may be more than one search goal behind a certain query.
In recent years, web users often rely on search engines to deal with real-life complex tasks, such as wedding planning, travel planning or job hunting. However, conventional search engines intuitively consider a search query as a simple search task that can be accomplished by a single search goal. In fact, more and more queries are driven by complex tasks which include more than one subtask to be accomplished and thus force users to issue a series of subtask queries. For example, the complex task “prepare wedding” consists of multiple subtasks such as “buy wedding ring,” “reserve wedding venue,” and “rent wedding limousine.” Therefore, to accomplish this complex task, the users need to submit at least three subtask queries.
Furthermore, each subtask in a complex task may trigger a few consumption needs. To deal with subtasks consumption needs, users may try to seek sponsored ads when the web search results fail to fit their consumption needs. Guo and Agichtein (2010) mentioned that users searching on the web with consumption needs are more likely to click ads. Hillard et al. (2010) also pointed out that suggesting ideal ads may actually improve user search experience. However, given a complex task query, conventional search engines are usually not able to provide suitable ads covering the consumption needs derived from the subtasks of a complex search task.
To enhance the advertiser’s revenue, sponsored search systems should optimize the conversion rate of recommended ads. Although some recent studies focus only on increasing the ad-click rate based on the click history data, however, improving ad-click rate may not accurately reflect if the clicked ads truly trigger users’ consumption needs, and may not increase the conversion rate. In fact, some users probably click an ad due to curiosity and will not engage in any consumption in the advertised website. When a user decides to purchase or reserve something, the user probably achieved a consumption need. Therefore, suggesting attractive ads, which fit the user’s true desires, will also improve the conversion rate and decrease the search time for users. Furthermore, for advertisers, a larger conversion rate with smaller click rate will optimize revenue and create a win-win situation. To the best of our knowledge, our work is the first to address the issue of complex task advertising, while the above previous works only focus on single task advertising.
To help users to efficiently accomplish their complex search task, in this work, we first propose a search goal model based on the users’ search query. Then, we further propose a topic-event-based complex search task model to construct the structure of complex search tasks containing a complex task name and subtask names. Finally, based on our constructed complex task structures, we propose a consumption-need-based complex-task ad recommendation model. In this research work, we developed a prototype system task-aware integrated search engine to help users efficiently accomplish their complex search tasks.

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