本文關(guān)鍵詞： 在線社交網(wǎng)絡(luò) 多粒度 猶豫模糊語言 群偏好獲取方法 群推薦方法　出處：《合肥工業(yè)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：群推薦系統(tǒng)是為參與共同活動(dòng)的群體推薦滿足群中所有成員的共同愛好的信息系統(tǒng),現(xiàn)有成果主要支持線下已成團(tuán)群體。隨著在線社交網(wǎng)絡(luò)的出現(xiàn),具有相似興趣的群體形成了各類虛擬社區(qū),面向社交網(wǎng)絡(luò)中虛擬社區(qū)的群推薦系統(tǒng)將成為推薦領(lǐng)域的熱點(diǎn)問題。由于群推薦方法是群推薦系統(tǒng)的核心問題,因此研究群推薦理論與群推薦方法具有重要的理論意義和應(yīng)用價(jià)值。在群推薦系統(tǒng)中,由于不同的個(gè)體偏好不同,個(gè)體在描述這些偏好信息時(shí)習(xí)慣采用自然語言,所以其偏好信息通常具有模糊性、猶豫性的特點(diǎn)。此外,由于不同的網(wǎng)絡(luò)平臺(tái)上可能采用不同粒度的語言信息進(jìn)行推薦,所以群體偏好信息還具有多粒度性�；诖�,本文在猶豫模糊語言環(huán)境下,首先研究了在線社交網(wǎng)絡(luò)中的用戶群發(fā)現(xiàn)方法。其次,在研究了個(gè)體評(píng)分預(yù)測(cè)的基礎(chǔ)上,深入地研究了在線社交網(wǎng)絡(luò)中用戶群偏好獲取方法。最后,研究了面向在線社交網(wǎng)絡(luò)用戶的TOPSIS群推薦方法、VIKOR群推薦方法。具體研究工作與創(chuàng)新點(diǎn)如下:(1)多粒度猶豫模糊語言環(huán)境下的群體發(fā)現(xiàn)方法。群體發(fā)現(xiàn)是群推薦的基礎(chǔ)性問題,為此本部分首先在多粒度猶豫模糊語言術(shù)語集的基礎(chǔ)上,引入了多粒度猶豫模糊語言余弦相似性計(jì)算公式,采用余弦相似性公式計(jì)算用戶之間的相似性,分析余弦相似性與距離相似性公式之間的差異。其次,將最小生成樹方法拓展到多粒度猶豫模糊語言環(huán)境中用于聚類分析。最后,將最小生成樹聚類方法與等價(jià)關(guān)系聚類方法進(jìn)行對(duì)比,分析所提出的方法聚類分析中的合理性和有效性。(2)猶豫模糊語言環(huán)境下的評(píng)分預(yù)測(cè)方法。評(píng)分預(yù)測(cè)問題是群推薦系統(tǒng)中研究焦點(diǎn)之一,為此本部分首先綜述群推薦系統(tǒng)中評(píng)分預(yù)測(cè)的主要方法,闡述猶豫模糊語言環(huán)境下評(píng)分預(yù)測(cè)的必要性。其次,在猶豫模糊語言環(huán)境下距離相似性公式、余弦相似性公式的基礎(chǔ)上提出相關(guān)系數(shù)相似性公式用于計(jì)算用戶之間的相似性。最后,采用距離相似性公式、余弦相似性公式和相關(guān)系數(shù)相似性公式對(duì)算例進(jìn)行計(jì)算,預(yù)測(cè)出未知的評(píng)分信息,比較三種方法預(yù)測(cè)的精度,分析所提出的相關(guān)系數(shù)相似性公式對(duì)猶豫模糊語言信息評(píng)分預(yù)測(cè)的可行性和有效性。(3)多粒度猶豫模糊語言的群偏好獲取方法。在群推薦系統(tǒng)中如何將個(gè)體偏好信息集結(jié)成群體偏好信息是一個(gè)關(guān)鍵問題,為此本部分首先提出三角形猶豫模糊集的概念,分析三角形猶豫模糊集的性質(zhì),采用三角形猶豫模糊集對(duì)多粒度猶豫語言進(jìn)行轉(zhuǎn)換。其次,定義廣義三角形猶豫模糊有權(quán)平均算子和廣義三角形猶豫模糊有權(quán)幾何算子,推導(dǎo)出這兩個(gè)算子的性質(zhì)。最后以汽車推薦為例,利用這兩個(gè)算子對(duì)多粒度猶豫模糊語言信息描述的群偏好問題進(jìn)行集結(jié),分析該模型解決群體偏好獲取是合理和有效的。(4)多粒度猶豫模糊語言的TOPSIS群推薦方法。針對(duì)不同群體的偏好信息具有多粒度性、猶豫模糊性等特點(diǎn),本文首先定義多粒度猶豫模糊語言術(shù)語集的概念,定義多粒度猶豫模糊語言的系列距離公式,研究這些公式的性質(zhì),討論公式之間的關(guān)系。其次,在屬性權(quán)重完全未知的情況下,建立目標(biāo)規(guī)劃模型,利用拉格朗日方程求解模型得到屬性權(quán)重;在屬性權(quán)重不完全未知的情況下,采用線性規(guī)劃模型求解屬性權(quán)重。最后,將這些距離公式結(jié)合TOPSIS方法用于群體推薦問題,并分析公式的參數(shù)對(duì)TOPSIS方法的滿意度及推薦結(jié)果的影響情況。(5)多粒度猶豫模糊語言信息的VIKOR群推薦方法。針對(duì)群推薦系統(tǒng)中被推薦項(xiàng)目具有多粒度性、猶豫模糊性問題,本文首先在多粒度猶豫模糊語言術(shù)語集的基礎(chǔ)上,引入多粒度猶豫模糊語言信息熵的概念及計(jì)算公式,采用信息熵公式計(jì)算被推薦項(xiàng)目的屬性權(quán)重;其次,將傳統(tǒng)的VIKOR方法拓展到多粒度猶豫模糊領(lǐng)域,并對(duì)其妥協(xié)解公式進(jìn)行改進(jìn),將改進(jìn)的VIKOR方法用于群推薦;最后,從理論分析、數(shù)值計(jì)算和敏感性分析3個(gè)方面將VIKOR方法與TOPSIS方法進(jìn)行對(duì)比,分析所提出的方法在群推薦應(yīng)用中的合理性和有效性。總之,本文在多粒度猶豫模糊語言環(huán)境下,研究了在線社交網(wǎng)絡(luò)用戶的群發(fā)現(xiàn)方法、用戶群偏好獲取方法和群推薦方法,對(duì)群推薦系統(tǒng)中的關(guān)鍵問題進(jìn)行了深入、系統(tǒng)的研究,研究成果不僅拓展了模糊數(shù)學(xué)理論,同時(shí)對(duì)群推薦等群決策問題具有指導(dǎo)意義。
[Abstract]:Group recommendation system is the common information recommendation system of all members of the group in order to meet to participate in the common activities of the group, the existing main achievements under the support line has become a group of groups. With the emergence of online social networks, with similar interest groups formed of various types of virtual communities, to group recommendation system of virtual community in social network will has become a hot issue in the field of group recommendation. The recommended method is a core problem in group recommendation system, so it has important theoretical significance and application value to research the theory and method of group recommendation group recommendation. In group recommendation system, due to different individual preferences of different individual habits, the use of natural language in describing the preference information, so the the preference information usually is fuzzy, the characteristics of hesitation. In addition, due to the different network platform may recommend the use of language information with different granularity, so The preference information also has multi granularity. Based on this, this article in hesitant fuzzy language environment, firstly, users found in online social networks. Secondly, based on the individual rating prediction, in-depth study of the preference of users in online social network acquisition method. Finally, the research oriented online social network user group TOPSIS recommendation method, recommended VIKOR group. The specific research work and innovations are as follows: (1) multi granularity hesitant fuzzy language environment. The group found that the group found that the method is recommended the fundamental problem, so this part of the multi granularity based hesitation fuzzy linguistic terms set. The introduction of multi granularity hesitant fuzzy language cosine similarity formula. The similarities between users using cosine calculation formula, analysis between similarity and cosine distance similarity formula Difference. Secondly, minimum spanning tree method is extended to multi granularity clustering analysis for hesitant fuzzy language environment. Finally, comparing the relationship between the minimum spanning tree clustering method and clustering method, clustering analysis method proposed in this paper is reasonable and effective. (2) prediction method for hesitant fuzzy language environment score the problem is the focus of research. The prediction score of group recommendation system, the main method of this part of the first group recommendation system score, necessity of hesitant fuzzy language environment prediction. Secondly, fuzzy language under the environment of distance similarity formula in hesitation, based on the cosine similarity formula proposed the correlation coefficient of similarity the formula for computing the similarity between users. Finally, the similarity distance formula, cosine similarity formula and the correlation coefficient of similarity calculation formula The calculation, predict the unknown information prediction score, compare the three methods of precision, correlation coefficient analysis the feasibility of similar formula for predicting hesitant fuzzy language information and effectiveness. (3) multi granularity hesitation group preference fuzzy language acquisition method. In the group recommendation system to individual preference information aggregated into group preference information is a key problem, this part first proposes the concept of triangle hesitant fuzzy sets, analysis of properties of triangle hesitant fuzzy set using triangular fuzzy sets to hesitate, hesitate to convert multi granularity language. Secondly, the definition of generalized triangle fuzzy weighted average operator and hesitation hesitant fuzzy generalized triangle right geometric operator nature, the two operators are derived. Finally, to recommend car as an example, using the two operator fuzzy language description hesitate to ask for multi granularity partial group Questions by aggregating group preference acquisition analysis solution is reasonable and effective. The model (4) multi granularity hesitate method recommended fuzzy language TOPSIS group. With multi granularity according to different groups of preference information, hesitant fuzzy characteristics, the paper firstly defines the concept of multi granularity fuzzy linguistic terms set hesitation, defined the size of the distance formula of fuzzy language series of hesitation, properties of these formulas, discussed the relationship between the formula. Secondly, the attribute weights are completely unknown, the establishment of multi-objective programming model, using Lagrange equation model to obtain attribute weights; in the incomplete attribute weights are unknown, using linear programming models to compute the weights of attributes. Finally, the distance formula combined with TOPSIS method for group recommendation problem, and to analyze the impact of satisfaction and recommendation results the parameters of the formula of TOPSIS method. . (5) multi granularity fuzzy linguistic information recommendation method. VIKOR group. In group recommendation system recommended by the project with multi granularity, hesitant fuzzy problem, firstly, hesitate in the basis of fuzzy multi granularity linguistic term set, introducing multi granularity hesitate concept and formula of fuzzy language information entropy, using the formula the information entropy calculation recommended attribute weights of the project; secondly, the traditional VIKOR method is extended to multi granularity fuzzy field and the hesitation, the compromise solution formula was improved, the improved VIKOR method for group recommendation; finally, from the theoretical analysis, numerical calculation and sensitivity analysis of the 3 aspects of the VIKOR method and TOPSIS method comparative analysis method, proposed in the group recommended the application of rationality and validity. In a word, based on the multi granularity hesitant fuzzy language environment, the online social network user group is Method, user group preference acquisition method and group recommendation method, have carried out in-depth and systematic research on the key issues of group recommendation system. The research results not only expand the fuzzy mathematics theory, but also have guiding significance for group recommendation and other group decision making problems.

【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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