使用者需求的預測是現在重要的議題之一，知道使用者的需求就可以根據使用者的需求給予相關的資料與幫助。在我們的研究中，我們想要根據使用者想要購買商品的商品查詢詞來預測使用者的購買需求並推薦適合的活動與搭配商品。例如，有個使用者想要購買跑鞋而在搜尋引擎上搜尋「跑鞋」找相關商品，我們推測此使用者是因為要參加馬拉松或是有慢跑的習慣因此要買跑鞋，因此，「馬拉松」、「慢跑」為我們預測的使用者需求，根據「馬拉松」、「慢跑」這個使用者需求我們推薦「馬拉松比賽」、「慢跑比賽」與「運動手環」、「毛巾」等搭配商品給使用者。
我們觀察到部落格文章關於購買商品的有可能含有使用者需求。除此之外，我們也發現拍賣網站上的商品標題也含有使用者需求。因此我們提出利用分析商品查詢詞、部落格文章與拍賣網站上的商品標題來預測使用者需求。在我們的研究中，我們將使用者需求分為三個面向，分別為Style，Entity 與 Action。我們利用條件隨機域來標記部落格文章與商品標題進而找出這三個面向的使用者需求，根據需求找出合適的活動與搭配商品並推薦給使用者。

User need prediction is an important problem, as it will largely improve user experience such as saving user efforts. In our work, we want to predict users’ purchasing needs according to queries about purchasing goods and recommend suitable activity-goods pairs. For example, a user wants to purchase running shoes and issues the query “running shoes” on a search engine to search goods. We surmise that this user purchase running shoes for running a marathon or having a jogging habit. Therefore, “marathon” and “jogging” are user needs that we predict. Moreover, for user needs “marathon” and “jogging”, we recommend activities such as “marathon competitions” and “jogging competitions” and matching goods such as “sport wristbands” and “towels” to the user.
We observed blog articles and found that blog articles about purchasing goods are always contain user needs. Moreover, titles from auction websites are contain user needs as well. Therefore, we proposed to predict user needs via analyzing queries, blog articles and titles from auction websites. In our work, We classified user needs in to three aspects: style, entity and action. We employed Conditional Random Field (CRF) to label blog articles and titles rom auction websites and further discover user needs of the three aspects. Further, we find suitable activity-goods pairs and recommend them to users.

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