融入社交關系與信任關系的移動應用推薦方法
發(fā)布時間：2018-09-07 09:24
【摘要】：移動互聯(lián)網(wǎng)環(huán)境下,移動應用信息過載是亟待解決的問題。因此,個性化推薦技術成為解決移動應用信息過載的重要途徑。傳統(tǒng)推薦方法存在數(shù)據(jù)稀疏、冷啟動等問題,采用余弦,Pearson方法計算相似度,當最近鄰沒有對待預測項目進行評分時,認為該用戶對預測結果沒有影響,從而影響推薦準確度。社交網(wǎng)絡與用戶信任關系是目前研究的熱點,本文綜合考慮了用戶的社交關系,偏好及信任關系,提出一種融合用戶社交關系與用戶信任關系的移動應用推薦方法。該方法融合社交關系,集贊與標簽等特征以及用戶對應用的偏好計算相似度,利用基于熟人的信任關系與用戶聲譽計算信任度,并通過合理的將相似關系與信任關系融合進行應用的推薦,提出的方法能有效提高推薦準確度。本文的研究內(nèi)容主要包括以下幾個方面:(1)本文設計實現(xiàn)了移動應用偏好度計算方法。傳統(tǒng)的移動應用偏好計算是直接基于用戶使用的頻次計算,這種方式?jīng)]有考慮某些用戶使用次數(shù)很多但是使用時長很少的情況,比如用,戶使用次數(shù)較多,但是每次點開后使用時間很短,此時并不能認為用戶真正喜歡該應用。本文綜合考慮了使用頻次和使用時長兩方面,通過線性加權求和的方式計算用戶對每個使用過的應用的偏好度。本文將用戶對應用的偏好度代替?zhèn)鹘y(tǒng)的用戶-項目評分,在一定程度上降低了數(shù)據(jù)稀疏性。(2)本文提出了基于社交關系評分預測模型。利用用戶偏好度、用戶社交相似度、社交互動行為,結合微信社交網(wǎng)絡的特征,綜合得到基于社交關系的評分預測模型。(3)本文提出了基于用戶信任關系的評分預測模型。通過研究分析社交網(wǎng)絡下用戶信任傳播機制得到基于社交關系的信任;通過微信社交網(wǎng)絡的特點,構建微信社交網(wǎng)絡下用戶特征文檔,從而得到用戶基于聲譽的信任,綜合這兩方面信任得到最終的基于用戶信任關系預測模型。
[Abstract]:Under the environment of mobile Internet, information overload of mobile application is an urgent problem to be solved. Therefore, personalized recommendation technology has become an important way to solve the information overload of mobile applications. The traditional recommendation method has some problems such as sparse data, cold start, etc. When the nearest neighbor does not score the prediction items, it is considered that the user has no influence on the prediction results, thus affecting the recommendation accuracy. The relationship between social network and user trust is a hot topic at present. This paper considers the social relationship preference and trust relationship of users and proposes a mobile application recommendation method which combines user social relationship and user trust relationship. The method combines the features of social relations, likes and tags, and the users' preferences to calculate the similarity. The trust degree is calculated by using the trust relationship based on acquaintance and the reputation of users. The proposed method can effectively improve the accuracy of recommendation by applying the fusion of similarity relationship and trust relationship. The main contents of this paper are as follows: (1) this paper designs and implements a method to calculate the preference degree of mobile applications. The traditional calculation of mobile application preference is directly based on the frequency calculation used by the user. This method does not take into account the situation that some users use a lot of times but use a few hours, for example, the number of times used by the user is more than that of the user. However, after each point of use is very short, this time can not be considered that the user really like the application. In this paper, the frequency and duration of use are considered synthetically, and the user's preference for each used application is calculated by the method of linear weighted summation. In this paper, the user preference degree is replaced by the traditional user-item score, which reduces the data sparsity to a certain extent. (2) this paper proposes a prediction model based on social relationship score. By using user preference, user social similarity, social interaction behavior and the features of WeChat social network, a score prediction model based on social relationship is obtained. (3) this paper proposes a score prediction model based on user trust relationship. Through the research and analysis of user trust communication mechanism under social network to get trust based on social relationship, through the characteristics of WeChat social network, build user characteristic document under social network of WeChat, so as to get user trust based on reputation. Combining these two aspects of trust, the final prediction model based on user trust relationship is obtained.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3;C912.3
本文編號：2227850
[Abstract]:Under the environment of mobile Internet, information overload of mobile application is an urgent problem to be solved. Therefore, personalized recommendation technology has become an important way to solve the information overload of mobile applications. The traditional recommendation method has some problems such as sparse data, cold start, etc. When the nearest neighbor does not score the prediction items, it is considered that the user has no influence on the prediction results, thus affecting the recommendation accuracy. The relationship between social network and user trust is a hot topic at present. This paper considers the social relationship preference and trust relationship of users and proposes a mobile application recommendation method which combines user social relationship and user trust relationship. The method combines the features of social relations, likes and tags, and the users' preferences to calculate the similarity. The trust degree is calculated by using the trust relationship based on acquaintance and the reputation of users. The proposed method can effectively improve the accuracy of recommendation by applying the fusion of similarity relationship and trust relationship. The main contents of this paper are as follows: (1) this paper designs and implements a method to calculate the preference degree of mobile applications. The traditional calculation of mobile application preference is directly based on the frequency calculation used by the user. This method does not take into account the situation that some users use a lot of times but use a few hours, for example, the number of times used by the user is more than that of the user. However, after each point of use is very short, this time can not be considered that the user really like the application. In this paper, the frequency and duration of use are considered synthetically, and the user's preference for each used application is calculated by the method of linear weighted summation. In this paper, the user preference degree is replaced by the traditional user-item score, which reduces the data sparsity to a certain extent. (2) this paper proposes a prediction model based on social relationship score. By using user preference, user social similarity, social interaction behavior and the features of WeChat social network, a score prediction model based on social relationship is obtained. (3) this paper proposes a score prediction model based on user trust relationship. Through the research and analysis of user trust communication mechanism under social network to get trust based on social relationship, through the characteristics of WeChat social network, build user characteristic document under social network of WeChat, so as to get user trust based on reputation. Combining these two aspects of trust, the final prediction model based on user trust relationship is obtained.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3;C912.3
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