本文選題：個性化推薦系統(tǒng) + 免疫算法　； 參考：《天津工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)與信息技術(shù)的迅猛發(fā)展,人類已經(jīng)進入到了一個數(shù)據(jù)爆炸的時代。在生活工作中,面對大量的信息,卻無法從中獲得對自己真正有用的信息,信息的使用效率反而降低了,這無疑會使淹沒在信息海洋中的用戶感到無比的苦惱。于是,如何能夠快速高效地從海量數(shù)據(jù)中找出對自己有用的信息已經(jīng)迫在眉睫。推薦系統(tǒng)是解決這一問題的有效手段,它能夠很好地為用戶提供若干條有用的推薦,被認為是緩解信息過載最有潛力的工具。通常,傳統(tǒng)推薦系統(tǒng)的主要目的是使精確度最大化。但是,隨著用戶數(shù)量和產(chǎn)品的規(guī)模巨增,只考慮準確性已經(jīng)完全不能滿足用戶的需求。個性化推薦系統(tǒng)是為每一個用戶都"量身定做"推薦,并且考慮用戶的多種需求。在個性化推薦系統(tǒng)中準確性和多樣性是兩個相互制約的性能指標,提高推薦的準確度無疑會有損推薦的多樣性;同樣,向用戶推薦多樣化的項目也會導(dǎo)致推薦準確性的降低。因此,在同時優(yōu)化推薦準確度和多樣性的過程中,需要找到一個適當(dāng)?shù)钠胶狻Ｃ庖邇?yōu)化算法是一種有效的智能算法,通過模擬免疫系統(tǒng)的原理和功能來解決復(fù)雜問題,在局部搜索和全局搜索中顯示出優(yōu)越的性能,為多目標優(yōu)化問題提供了 一種新的求解思路。本文對個性化推薦系統(tǒng)中的推薦技術(shù)進行了探索和研究,提出了基于多目標免疫算法的網(wǎng)絡(luò)個性化推薦。該方法利用了生物免疫系統(tǒng)的基本原理,將要求解的個性化推薦列表建模成一個最大化推薦準確性和多樣性的多目標優(yōu)化問題,設(shè)計了適合個性化推薦問題求解的抗體編碼方式、克隆算子、變異算子。仿真實驗結(jié)果表明,該算法能夠有效的得到個性化推薦的最佳解,提高了準確性和多樣化,達到可以同時為多個用戶提供多個不同推薦的需求。
[Abstract]:With the rapid development of Internet and information technology, mankind has entered a data explosion era. In life and work, faced with a large amount of information, but can not get their own real useful information, the efficiency of the use of information is reduced, which will undoubtedly cause inundation of users in the ocean of information. Therefore, how to quickly and efficiently find useful information from massive data is urgent. Recommendation system is an effective means to solve this problem. It can provide users with several useful recommendations and is considered to be the most potential tool to ease information overload. In general, the main purpose of traditional recommendation systems is to maximize accuracy. However, with the increase of the number of users and the scale of products, the accuracy alone can not meet the needs of users. Personalized recommendation system is for each user "tailor-made" recommendation, and take into account the user's various needs. In the personalized recommendation system, accuracy and diversity are two mutually restricted performance indicators, so improving the accuracy of recommendation will undoubtedly damage the diversity of recommendations. Similarly, the diversification of recommendation items to users will also lead to the reduction of recommendation accuracy. Therefore, a proper balance needs to be found in the process of optimizing the accuracy and diversity of recommendations at the same time. Immune optimization algorithm is an effective intelligent algorithm, which can solve complex problems by simulating the principle and function of immune system, and shows excellent performance in local search and global search. It provides a new method for solving multi-objective optimization problems. In this paper, the recommendation technology in personalized recommendation system is explored and studied, and the network personalized recommendation based on multi-objective immune algorithm is proposed. Based on the basic principles of biological immune system, the personalized recommendation list is modeled as a multi-objective optimization problem that maximizes recommendation accuracy and diversity. The antibody coding method, clone operator and mutation operator are designed for solving personalized recommendation problem. Simulation results show that the algorithm can effectively obtain the best solution of personalized recommendation, improve the accuracy and diversity, and can provide multiple different recommendations for multiple users at the same time.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3;TP18

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