本文選題：推薦算法　切入點(diǎn)：知識(shí)推薦　出處：《齊魯工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今社會(huì)環(huán)境下,優(yōu)秀的企業(yè)積累了很多歷史數(shù)據(jù),這些歷史數(shù)據(jù)包含豐富的經(jīng)驗(yàn)和知識(shí)。優(yōu)秀企業(yè)會(huì)特別注重這些歷史數(shù)據(jù),因?yàn)闅v史數(shù)據(jù)中往往包含著某些重要信息和行業(yè)發(fā)展趨勢(shì),我們把包含重要信息的歷史數(shù)據(jù)稱之為企業(yè)知識(shí),這些也就是企業(yè)的財(cái)富。隨著互聯(lián)網(wǎng)技術(shù)的發(fā)展與普及,知識(shí)傳播的速度越來越快,企業(yè)知識(shí)呈現(xiàn)指數(shù)級(jí)增長(zhǎng),員工在單位時(shí)間內(nèi)獲取到合適的知識(shí)的效率越來越低。因?yàn)橹R(shí)庫(kù)存儲(chǔ)的知識(shí)越來越多,知識(shí)庫(kù)的利用率也就慢慢下降。到目前為止,有效解決此問題的技術(shù)可以分為以推薦系統(tǒng)為代表的信息過濾技術(shù)和以搜索引擎為代表的信息檢索技術(shù)。本文主要研究的是前者。隨著電子商務(wù)的發(fā)展和個(gè)性化推薦系統(tǒng)的問世,有許多推薦算法被提出,而且在特定領(lǐng)域都發(fā)揮著不可替代的作用,比如說應(yīng)用在電子商務(wù)領(lǐng)域中的協(xié)同過濾算法。雖然此算法被廣泛應(yīng)用,但是其仍然存在著諸如數(shù)據(jù)稀疏問題、冷啟動(dòng)等問題。隨著企業(yè)知識(shí)庫(kù)的飛速發(fā)展,企業(yè)知識(shí)的推薦也逐漸成為最近的研究熱門。如何有效的利用相關(guān)技術(shù)來改進(jìn)推薦算法的性能和提高企業(yè)知識(shí)推薦質(zhì)量也日益被科研人員廣泛研究。針對(duì)現(xiàn)有的問題,本文在以下兩個(gè)方面展開研究。第一,關(guān)于馬爾可夫預(yù)測(cè)模型的研究。本文針對(duì)用戶-項(xiàng)目之間的聯(lián)系,認(rèn)為用戶本次查看項(xiàng)目行為和下次查看行為是有強(qiáng)聯(lián)系的,基于這個(gè)設(shè)想提出一種基于馬爾可夫預(yù)測(cè)模型的協(xié)同過濾推薦算法。實(shí)驗(yàn)結(jié)果顯示,此算法具有較好的推薦效果。第二,關(guān)于粗糙集理論與企業(yè)知識(shí)的相關(guān)研究。針對(duì)員工的需要自動(dòng)為其推薦恰當(dāng)?shù)闹R(shí),可以提高員工的工作效率和員工與公司的粘度,同時(shí)可以提高企業(yè)知識(shí)的應(yīng)用,對(duì)于員工和企業(yè)都是有利的。把員工需求用粗糙集理論表示出來,然后把知識(shí)庫(kù)中的知識(shí)與其模型相匹配,得到推薦結(jié)果。此模型可以充分利用知識(shí)庫(kù)中的知識(shí),有利于企業(yè)的發(fā)展與進(jìn)步。
[Abstract]:In today's social environment, excellent enterprises have accumulated a lot of historical data. These historical data contain rich experience and knowledge. Good enterprises pay special attention to these historical data. Because historical data often contain some important information and industry trends, we call historical data containing important information as corporate knowledge, which is the wealth of enterprises. With the development and popularization of Internet technology, The speed of knowledge transmission is getting faster and faster, the enterprise knowledge is increasing exponentially, and the efficiency of obtaining the appropriate knowledge in a unit of time is becoming increasingly low, because the knowledge base stores more and more knowledge, The utilization of the knowledge base is declining. So far, The effective technology to solve this problem can be divided into the information filtering technology represented by recommendation system and the information retrieval technology represented by search engine. The former is mainly studied in this paper. With the development and individuation of electronic commerce. The advent of the recommendation system, Many recommended algorithms have been proposed and play an irreplaceable role in specific areas, such as collaborative filtering algorithms used in electronic commerce. Although this algorithm is widely used, However, there are still some problems such as sparse data, cold start, etc. With the rapid development of enterprise knowledge base, How to improve the performance of recommendation algorithm and improve the quality of enterprise knowledge recommendation has been widely studied by researchers. In this paper, the following two aspects of the study. First, the study of Markov prediction model. According to the relationship between the user and the project, this paper thinks that there is a strong relationship between the behavior of the user viewing the project and the next viewing behavior. Based on this assumption, a collaborative filtering recommendation algorithm based on Markov prediction model is proposed. The experimental results show that the algorithm has a good recommendation effect. Second, The relevant research on rough set theory and enterprise knowledge. According to the needs of employees, it can automatically recommend appropriate knowledge, which can improve the efficiency of employees and the viscosity between employees and companies, at the same time, it can improve the application of enterprise knowledge. It is beneficial to both employees and enterprises. The employee needs are expressed by rough set theory, then the knowledge in the knowledge base is matched with its model, and the recommended results are obtained. This model can make full use of the knowledge in the knowledge base. It is conducive to the development and progress of enterprises.
【學(xué)位授予單位】：齊魯工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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