結合內容及協同過濾於個人化求職推薦

Abstract

近年來隨著資訊產業及網際網路蓬勃發展，傳統透過平面媒體求職的時代已經式微，反之經由網路線上化的方式已取而代之。透過線上化可以即時查詢所喜愛的工作項目，節省了時間及金錢上的成本。不過由於求才項目眾多，對於求職者來說依舊必須透過傳統方法循序地尋找喜愛的工作或是經由系統幫助，透過求職者履歷進行工作媒合的服務。而本研究導入推薦系統的概念，提出結合內容及協同過濾個人化推薦模型來進一步提升工作媒合效能以及提供個人化推薦服務，並應用於國內某知名人力銀行進行推薦媒合驗證。本研究模型首先透過使用者喜好輪廓資料(profile)進行內容篩選過濾，產生候選推薦清單，並透過使用者求職記錄將使用者分群，最後應用項目為主(Item-based)協同過濾技術並透過求才項目的求職數來導出結合項目協同相似性以及項目內容相似性迴歸方程式，進而來產生出推薦清單。\n本研究透過準確度(Precision)、喚回度(Recall)以及F1評估準則與過去研究方法及人力銀行應用方法進行比較。其實驗結果顯示，本研究所提出的模型顯著地優於傳統結合內容與協同過濾技術、個別過濾技術以及人力銀行現行應用技術。

With the rapid development of information technology and popularization of the internet, traditional job-finding services have declined and replaced by the on-line job service. Job seekers can query job vacancy information they like easily and economically by using on-line job finding service. However, due to huge job vacancy information, job seekers still have to examine those jobs manually or through matching system which provides job matching service based on resumes. In this paper, we introduce recommender system concept, propose a hybrid model-based recommender that achieves better performance of job matching and provides personalized service. We use data from a famous domestic job hunting company to evaluate our model. Our hybrid model first processes content filtering using user profile to produce candidate recommendations. We also partition users based on submitting resume records. Then, we use item-based collaborative filtering to compute item similarities and content item similarities. Finally, we find a regression formula to combine the similarities to produce top N recommendations. The experimental results show that our system can improve the recommendation performance significantly. Besides, our system also achieves better performance when comparing with other combining approaches.