本文關(guān)鍵詞： 推薦系統(tǒng) 推薦算法 旅游攻略 覆蓋度 協(xié)同過濾　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：大數(shù)據(jù)的逐步發(fā)展使人類邁入了信息過載的時代,用戶怎么樣才能在紛繁復(fù)雜的信息中擇優(yōu)選出他們所需的信息,信息又如何有效的呈現(xiàn)在用戶面前呢,這無疑給計算機工作者帶來了新的挑戰(zhàn)。為解決此問題,推薦系統(tǒng)隨之而生。它是解決信息過載問題的重要方法,并且與搜索引擎相比,它可以為其私人訂制,滿足每一個用戶的獨特需求。用戶在使用網(wǎng)站時的行為數(shù)據(jù)會被記錄下來,推薦系統(tǒng)通過分析這些數(shù)據(jù)獲取每一個用戶興趣所在,從而投其所好,為每一個用戶尋得他們需要的信息。如今,推薦系統(tǒng)被應(yīng)用的領(lǐng)域越來越廣泛,其商用價值也越來越大,也得到了學(xué)術(shù)界越來越多的關(guān)注和研討,不僅在理論上有很大提升,更在實踐方面有質(zhì)的飛躍,逐步形成了一門獨立的學(xué)科。在新的時代,推薦系統(tǒng)也面臨一系列挑戰(zhàn)。論文首先論述了推薦系統(tǒng)的研究背景及意義,并介紹其在國內(nèi)外的研究現(xiàn)狀。之后詳細講解并比較了兩個基本算法,首先介紹的一種協(xié)同過濾推薦算法是基于用戶的,隨后本文又通過案例分析介紹了另一種協(xié)同過濾推薦算法,該算法是基于物品的[1]。緊接著在下一章中介紹了用于評價推薦系統(tǒng)好壞的評測指標。最后,本文針對旅游攻略提出了基于混合協(xié)同過濾的旅游攻略推薦算法。旅游攻略是旅行市場的一款產(chǎn)品,由用戶創(chuàng)建用戶閱覽,優(yōu)質(zhì)攻略平均每篇文字數(shù)萬,覆蓋多個城市,這些一般是相鄰的城市,如果是國外,一般同一個國家也有少數(shù)跨同一個洲。每篇攻略都包含標題、內(nèi)容、旅游城市、游玩景點,其中的旅游城市是攻略推薦中最核心的三個因素之一。本文研究的基于混合協(xié)同過濾的旅游攻略推薦系統(tǒng)是針對旅游市場設(shè)計的個性化推薦系統(tǒng),結(jié)合兩種基本算法,經(jīng)過不斷分析與實驗,通過優(yōu)化推薦度來對算法加以改進,以提高覆蓋度。主要改進的地方有兩點,第一點是對用戶和物品的興趣度加以平衡,第二點是加入城市熱度和攻略熱度的參數(shù),并對其加以懲罰,目的是使熱度高的城市或攻略推薦度低一些,使那些冷門的城市或攻略能更容易的被推薦出去。本文對基于旅游用戶、旅游攻略和旅游城市推薦的推薦度分別進行了計算,然后平衡三個推薦度得到最終的推薦度。綜合考慮了旅游攻略的數(shù)據(jù)特點和推薦過程的特殊性,極大程度增加推薦攻略的覆蓋度并解決“長尾效應(yīng)”的推薦問題,提出了一個針對旅游攻略的極具特色的推薦系統(tǒng)。
[Abstract]:Big data's gradual development makes people enter the era of information overload, how can users choose the information they need in the complicated information, and how to effectively present the information in front of the users? This undoubtedly brings a new challenge to computer workers. In order to solve this problem, recommendation system comes into being. It is an important method to solve the problem of information overload, and compared with search engine, it can be customized for private users. To meet the unique needs of each user. The user behavior data in the use of the site will be recorded, the recommendation system through the analysis of these data to obtain each user interest, so as to take advantage of it. Nowadays, recommendation system has been applied more and more widely, and its commercial value has become more and more great, and it has also been paid more and more attention and research in academia. Not only in theory there is a great improvement, but also in practice there is a qualitative leap, gradually formed an independent discipline. In the new era. Recommendation system also faces a series of challenges. Firstly, this paper discusses the research background and significance of recommendation system, and introduces its research status at home and abroad. Then, two basic algorithms are explained and compared in detail. This paper first introduces a collaborative filtering recommendation algorithm based on user, and then introduces another collaborative filtering recommendation algorithm by case study, which is based on articles. [1. Then in the next chapter, the evaluation index used to evaluate the quality of recommendation system is introduced. Finally. This paper puts forward a recommendation algorithm of tourism strategy based on mixed collaborative filtering. Tourism strategy is a product of the travel market. Users create users to read, and the average quality of each article is tens of thousands of words. Covers many cities, these are generally adjacent cities, if abroad, generally the same country has a few across the same continent. Each strategy contains the title, content, tourist cities, tourist attractions. The tourism city is one of the three core factors in the strategy recommendation. The tourism strategy recommendation system based on mixed collaborative filtering is a personalized recommendation system designed for the tourism market. Combined with two basic algorithms, through continuous analysis and experiments, the algorithm is improved by optimizing the recommendation degree to improve the coverage. There are two main improvements. The first is to balance the interest of the user and the object, and the second is to add and punish the parameters of urban heat and strategy heat, with the aim of making cities with high heat or strategic recommendations lower. So that those unpopular cities or strategies can be more easily recommended out. This paper calculates the recommended degree based on tourism users, tourism strategy and tourism city recommendation. Then the final recommendation degree is obtained by balancing the three recommended degrees. The characteristics of the data of tourism strategy and the particularity of the recommendation process are considered synthetically. By greatly increasing the coverage of the recommended strategy and solving the problem of "long tail effect", a special recommendation system for tourism strategy is proposed.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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