隨著科技的進步，人工智慧已成為人力資源管理不可或缺的一部份。因此，若能借助人工智慧聊天機器人的應用，將有助於企業招募。企業資訊的揭露程度會影響求職者的決策與雙方的適配度。對於企業而言，招募合適的人才，並用於合適的職位，是相當重要的。

本研究以國立臺灣科技大學企業管理系之110學年度碩士班甄試生與考試生為例，將學校視為企業、指導教授視為企業主管、研究生視為求職者，用以探討招募之情境。本文研究設計先使用Python進行程式撰寫，建立一推薦聊天機器人，並利用深度訪談搜集各指導教授與實驗室之相關資訊，作為文本，再以自然語言處理技術為基礎，運用相似度分析建立模型，將碩士生之資訊與文本進行評分排序，並推薦相似度最高之五位指導教授，供碩士生參考，達成師生媒合推薦之目的。

本研究透由推薦聊天機器人進行資訊傳遞以及初步篩選，來改善碩士生之資訊不對稱程度，並利用科技接受模式來預測碩士生對推薦聊天機器人的接受程度。實驗以單組前後測進行，分別於使用推薦聊天機器人前與使用後，發放資訊不對稱與科技接受模式之量表問卷。

The rapid development of technology, Artificial Intelligence (AI) has become an indispensable part of human resource management. Therefore, using Artificial Intelligence Chatbots will conduce to the recruitment. The level of corporate information disclosure would affect the decision-making of job applicants and the level of adaptation between employer and employee. For enterprises, it is important to put the right talent in the right position.

This research takes the MBA students of 2021 at National Taiwan University of Science and Technology for example. We regard the school as a company, advisors as managers, and students as job seekers to discuss the corporate recruitment scenario. The experiment process uses Python for programming and building an Artificial Intelligence Chatbot. In-depth interviews were used to collect the information about each advisor and laboratory. Then, we employ the Natural Language Processing (NLP) technology, which involving in the application of the similarity to build the model. After analyzing and sorting the information of students, it recommends the five most similar advisors for students, which achieves the purpose of matching between advisors and students.

The research uses the Recommended Chatbots to convey information and preliminary screening. The aim is to improve the impacts of information asymmetry and predict user acceptance of the Recommended Chatbots by Technology Acceptance Model (TAM). The experiment was conducted by questionnaires, which about Information Asymmetry and Technology Acceptance Model, and adopted a one-group pretest-posttest design. The questionnaire was tested before and after using the Recommended Chatbots.

一、 中文文獻
賴宜弘、黃芬芬、楊雪華（2015）。科技接受模式中文版量表之編制與相關研究。亞東學報，35，201-222。
吳恩昕（2020）。資訊不對稱下之資訊收集對焦慮的影響〔未出版之碩士論文〕。國立臺灣科技大學企業管理系碩士班。
鄭寓恩（2021）。資訊提供對組織吸引力之影響－以資訊不對稱為中介變項〔未出版之碩士論文〕。國立臺灣科技大學企業管理系碩士班。
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