本文關鍵詞： 個性化就業(yè)推薦 協(xié)同過濾 k-means 信息增益率 高校畢業(yè)生　出處：《昆明理工大學》2015年碩士論文　論文類型：學位論文

【摘要】：隨著大學生畢業(yè)生不斷增加,就業(yè)難的問題日益凸顯。而大學生不能掌握有效就業(yè)信息、就業(yè)目標定位不準、準備不足都會對就業(yè)造成不利影響。面對就業(yè)網(wǎng)站大量的招聘信息以及學生和企業(yè)間的信息不對稱,使得畢業(yè)生難以搜索到適合自己的就業(yè)單位,只能盲目參加招聘會,這不僅浪費了時間和精力,甚至錯過了適合自己的就業(yè)機會,大大降低了就業(yè)成功率。面對現(xiàn)狀,雖然各高校都針對性的開展了就業(yè)指導和推薦,但是由于畢業(yè)生人數(shù)較多,高校無法針對每個畢業(yè)生的特點進行個性化推薦,而目前高校的就業(yè)網(wǎng)站只能發(fā)布就業(yè)信息,無法為畢業(yè)生推薦適合的就業(yè)單位。因此,我們需要尋找一種客觀、個性化且能針對個人情況進行推薦的方法和手段。隨著個性化推薦系統(tǒng)的研究和應用,為解決畢業(yè)生個性化就業(yè)推薦問題提供了有利支持。個性化就業(yè)推薦系統(tǒng)通過挖掘?qū)W生就業(yè)意向、職業(yè)興趣、在校表現(xiàn)等多方面信息,結(jié)合往屆生就業(yè)數(shù)據(jù),能夠為畢業(yè)生推薦適合的就業(yè)單位,引導畢業(yè)生進行有效的就業(yè)準備,減少時間和精力的浪費,提高就業(yè)成功率。目前在畢業(yè)生個性化就業(yè)推薦系統(tǒng)方面的研究尚不成熟,推薦效果有待提高,推薦模型和推薦算法仍需改進。本文對目前研究中存在的下述3個不足進行了研究：(1)缺乏結(jié)合學生就業(yè)特征的推薦。目前使用較多的是傳統(tǒng)協(xié)同過濾算法,僅僅依靠學生就業(yè)興趣評分,沒有考慮到學生特征對于就業(yè)的影響。(2)不能客觀的確定就業(yè)特征的影響權重。目前,特征權重確定大都采用主觀評價方法,難以體現(xiàn)客觀實際。(3)采用K-means聚類提高推薦速度時沒有解決該算法受初始聚類中心影響的問題。針對問題,本文首先分析了影響畢業(yè)生就業(yè)的因素,從中提取了9個學生就業(yè)特征；其次,通過對比分析選擇了信息增益率作為計算特征權重的方法；然后,為避免聚類效果受初始聚類中心的影響,提出了改進的AK-means (Adcanced-K-Means)算法對學生特征聚類,利用MATLAB編程驗證了算法的有效性,最終,結(jié)合學生就業(yè)特征和興趣評分,構造了基于學生特征的協(xié)同過濾就業(yè)推薦模型。在推薦模型建立的基礎上,采集了某工科學院4年的學生就業(yè)數(shù)據(jù)對模型進行驗證分析,并采用SQL Sever 2008數(shù)據(jù)庫和C#編程語言開發(fā)了基于B/S結(jié)構的就業(yè)推薦系統(tǒng)原型,該原型能夠?qū)崿F(xiàn)本文模型的推薦功能。通過驗證,本文提出的就業(yè)推薦模型具有一定的有效性,能夠為學生就業(yè)提供一定的參考作用,對于推薦系統(tǒng)在高校的應用具有積極的探索意義。
[Abstract]:With the increase of college graduates, the employment problem has become increasingly prominent. The students can't master the effective employment information, employment target positioning, lack of preparation may have an adverse impact on employment. Facing the employment site a lot of recruitment information and the information asymmetry between students and enterprises, makes it difficult for graduates to search for their own employment units, only blind to participate in the recruitment, which is not only a waste of time and energy, even missed their own jobs, and greatly reduce the success rate of employment. The face of the status quo, although all colleges and universities to carry out employment guidance and recommendations, but because the number of graduates in Colleges and universities are not according to the characteristics of each graduate's recommendation, and at present, college employment website can publish employment information, to recommend suitable employment for graduates. Because of this, we need to To find an objective, personalized and ways and means for personal recommendation. With the research and application of personalized recommendation system, and provide favorable support for solving the employment problem. Graduates personalized recommendation personalized employment recommendation system by mining employment intention, student occupation interest, school performance and other aspects of information, combined with the previous employment the data can be recommended for employment of graduates, graduates of effective employment preparation, reduce the waste of time and energy, improve the success rate of employment. At present in the graduates employment recommendation system research on personalized recommendation is not mature, the effect needs to be improved, the recommended model and recommendation algorithms still need to be improved. This paper made a research on the present research in the following 3 aspects: (1) the lack of employment characteristics of students recommended. Currently used more is traditional Collaborative filtering algorithms rely solely on students' Employment Interest score, without considering the characteristics of the students for employment effects. (2) effects of the employment characteristics determine the weight can not be objective. At present, most of the feature weights and subjective evaluation method, is difficult to reflect the objective reality. (3) using the K-means clustering algorithm does not solve the initial clustering center influence of increasing the recommended speed. To solve the problem, this paper first analyzes the factors affecting the employment of graduates, extracted 9 students employment characteristics; secondly, through the comparative analysis of the choice of the information gain rate as a method of feature weight calculation; then, in order to avoid the clustering effect is influenced by the initial cluster center, put forward the improved AK-means algorithm (Adcanced-K-Means) on the characteristics of the student clustering, using MATLAB programming to verify the effectiveness of the algorithm, finally, combined with the characteristics of students' employment and interest Score, structure of the collaborative filtering recommendation model based on the characteristics of students' employment. Based on the model, a collection of Engineering College Students' employment data for 4 years to verify the analysis of the model, and the development of the B/S structure of the employment recommendation system prototype based on using SQL Sever 2008 database and C# programming language, the recommendation function the prototype can realize this model. Through the verification, the proposed employment recommendation model has certain validity, can provide reference for the employment of students, the recommendation system is very significance to explore in the application.

【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：G647.38

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本文編號：1477237