發(fā)布時間：2018-05-05 09:21

  本文選題：推薦算法 + 協(xié)同過濾　； 參考：《鄭州大學(xué)》2017年碩士論文

【摘要】：做為處理信息過載的有效手段,推薦系統(tǒng)在近些年得到了廣泛的研究與發(fā)展,推薦系統(tǒng)在各領(lǐng)域應(yīng)用的成功案例也不斷涌現(xiàn),但是依然面臨著很多問題亟待解決。形式概念分析(Formal Concept Analysis,FCA)的核心數(shù)據(jù)結(jié)構(gòu)—概念格(Concept lattices),是一種數(shù)據(jù)分析與規(guī)則提取的有效工具。外延與內(nèi)涵做為概念的組成部分使得形式概念展現(xiàn)出了聚類的特性。概念之間存在的偏序關(guān)系也揭示了其泛化與特化的本質(zhì)。隨著其研究的不斷深入,形式概念分析開始逐步應(yīng)用于數(shù)據(jù)挖掘、信息檢索等領(lǐng)域。協(xié)同過濾(Collaborative Filtering,CF)推薦作為應(yīng)用最廣的推薦策略之一,其中經(jīng)典的基于鄰域的協(xié)同過濾算法通常只考慮用戶間或項目間的相似關(guān)系,而忽略了不同對象之間的內(nèi)在聯(lián)系。此外越來越多的研究人員發(fā)現(xiàn),推薦系統(tǒng)往往面對的是無法直觀反映用戶喜好程度的隱式數(shù)據(jù),并且隨著產(chǎn)品種類的劇增,用戶與項目間產(chǎn)生的隱式數(shù)據(jù)也會變得極為稀疏。所以由于稀疏數(shù)據(jù)環(huán)境下信息的缺失,協(xié)同過濾算法往往獲取不到充足的鄰域信息,從而直接影響了最終的推薦效果。針對以上問題,本文提出了一種面向隱式數(shù)據(jù)的基于概念鄰域的協(xié)同過濾推薦算法(Conceptual Neighborhood-based Collaborative Filtering,CNCF)。該算法針對Top-N推薦問題,以概念格為載體進行推薦問題求解。首先在用戶與項目的關(guān)系數(shù)據(jù)轉(zhuǎn)化而成形式背景的基礎(chǔ)上進行概念格的構(gòu)造,將用戶與產(chǎn)品分別以對象與屬性的形式聚集在概念中,并基于概念格生成相應(yīng)的起始概念索引,借助索引結(jié)構(gòu)高效地對對象的起始概念進行定位。之后利用概念之間的偏序關(guān)系,以對象(用戶)的起始概念為起點探索其近鄰概念并獲取候選項集。最后結(jié)合所提出的全局偏好度與鄰域偏好度篩選出用戶可能感興趣的推薦列表。通過對CNCF算法的實現(xiàn),并在兩個真實數(shù)據(jù)集上進行實驗驗證,相較于傳統(tǒng)基于鄰域的協(xié)同過濾推薦算法,CNCF算法在可以在保持較好的推薦效果同時,更適用于數(shù)據(jù)稀疏環(huán)境下的推薦。
[Abstract]:As an effective means to deal with information overload, recommendation system has been widely studied and developed in recent years. The successful cases of recommendation system in various fields are emerging, but there are still many problems to be solved. Formal Concept Analysis (FCA) is an effective tool for data analysis and rule extraction. Extension and connotation as part of the concept make the formal concept show the characteristics of clustering. The existence of partial ordering between concepts also reveals the essence of generalization and specialization. With the deepening of its research, formal concept analysis has been gradually applied to data mining, information retrieval and other fields. Collaborative filtering (CF) recommendation is one of the most widely used recommendation strategies, in which the classical neighborhood based collaborative filtering algorithms usually only consider the similarity between users or items, but ignore the internal relations between different objects. In addition, more and more researchers find that recommendation systems often face implicit data that can not directly reflect the degree of user preferences, and with the sharp increase in product types, the implicit data generated between users and projects will become extremely sparse. Therefore, because of the lack of information in sparse data environment, collaborative filtering algorithm often can not obtain sufficient neighborhood information, which directly affects the final recommendation effect. To solve the above problems, this paper proposes a Conceptual Neighborhood-based Collaborative filtering algorithm based on Conceptual Neighborhood-based Collaborative filtering for implicit data. The algorithm solves the Top-N recommendation problem with concept lattice as the carrier. Firstly, the concept lattice is constructed on the basis of transforming the relational data between the user and the project into a formal background. The user and the product are gathered in the concept in the form of objects and attributes respectively, and the corresponding initial concept index is generated based on the concept lattice. With the help of index structure, the starting concept of object is located efficiently. Then, by using the partial order relation between the concepts, starting from the initial concept of object (user), we explore the concept of nearest neighbor and obtain the set of candidate items. Finally, the proposed global preference degree and neighborhood preference degree are combined to filter out the list of recommendations that the user may be interested in. Through the implementation of CNCF algorithm and experimental verification on two real data sets, compared with the traditional collaborative filtering recommendation algorithm based on neighborhood, it can keep a good recommendation effect at the same time. More suitable for data sparse environment recommendation.
【學(xué)位授予單位】：鄭州大學(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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