基于位置社交網(wǎng)絡(luò)的個性化推薦方法的研究
發(fā)布時間：2018-08-03 12:07
【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的進一步發(fā)展,社交網(wǎng)絡(luò)也在人們的生活中越來越流行起來。人們通過移動社交網(wǎng)絡(luò)發(fā)現(xiàn)有用的生活信息,分享生活經(jīng)驗,與好友互動。并且伴隨著2010年前后安卓,蘋果等智能移動設(shè)備的應(yīng)用普及。在原有社交網(wǎng)絡(luò)的基礎(chǔ)上結(jié)合了用戶地理位置的相關(guān)信息,形成了一個新的社交網(wǎng)絡(luò)概念:基于位置的社交網(wǎng)絡(luò)(Location-based Social Network,LBSN)�；谖恢玫纳缃痪W(wǎng)絡(luò)不僅關(guān)注用戶線上發(fā)表的信息和用戶線上的好友關(guān)系,還保留了用戶線下的活動數(shù)據(jù)與活動模式,了解用戶的日常行為。由于LBSN包含了大量的多種類型的數(shù)據(jù)。我們可以對這些數(shù)據(jù)進行挖掘,發(fā)現(xiàn)有意義的信息。目前,對于利用LBSN數(shù)據(jù)對用戶進行個性化推薦的研究熱度非常高。利用LBSN數(shù)據(jù)對用戶推薦主要集中在以下三個領(lǐng)域:(1)興趣點推薦:這一推薦是針對用戶的,包括兩種推薦,一種是目的地點,另一種是多個地點結(jié)合的路線推薦;(2)商業(yè)選址推薦:這種推薦主要針對商戶;(3)好友推薦:對用戶進行線上好友推薦。盡管學(xué)者們對LBSN推薦系統(tǒng)的研究有了很大的突破,但目前還存在著以下一些問題:(1)興趣點與用戶眾多,計算量大,數(shù)據(jù)稀疏;(2)用戶關(guān)系表示方法過于簡單,包含內(nèi)容不多;(3)新加入用戶的冷啟動問題(4)沒有結(jié)合時空上下文信息等。針對以上問題,本文提出了一些新的想法與解決方案(1).通過劃分城市單元格,縮減問題分析規(guī)模,解決部分?jǐn)?shù)據(jù)稀疏問題;(2)將好友關(guān)系的表示豐富起來,更加符合現(xiàn)實世界。在普遍的LBSN數(shù)據(jù)中,好友關(guān)系只用01(有無)表示,顯然沒有考慮到好友關(guān)系的親密度,本文會結(jié)合線上線下數(shù)據(jù),對好友關(guān)系進行量化,從而對推薦系統(tǒng)起到更有意義的作用。(3)針對上下文信息的問題,本文提出結(jié)合時空信息,提出結(jié)合用戶時空特征的推薦,分析用戶與興趣點的時空特征以及當(dāng)前時空信息來做更好的推薦。另外,本文會結(jié)合線上信息和線下信息,考慮到實際區(qū)域的一些地理屬性,提出一種新的商業(yè)選址問題的想法和一種基于用戶與興趣點之間隨機游走的好友推薦模型。在試驗中,我們選取國外比較著名的LBSN數(shù)據(jù)集Foursquare進行分析與實驗,通過準(zhǔn)確率,召回率等一些評判標(biāo)準(zhǔn)比較文中的方法和之前的研究成果。對文章的算法進行一系列測試,能實現(xiàn)對用戶和POI供應(yīng)商的有效推薦。
[Abstract]:With the further development of Internet technology, social networks are becoming more and more popular in people's lives. People use mobile social networks to find useful life information, share life experiences, and interact with friends. And with Android, Apple and other smart mobile devices around 2010, the popularity of applications. Based on the existing social network, a new concept of social network, Location-based Social Network (LBSN), is formed by combining the relevant information of the user's geographical location. The location-based social network not only pays attention to the information published on the user line and the friends relationship on the user line, but also preserves the activity data and the activity pattern below the user line to understand the daily behavior of the user. Because LBSN contains a large number of types of data. We can mine the data and find meaningful information. At present, the research on personalized recommendation based on LBSN data is very hot. Using LBSN data to recommend users is mainly focused on the following three areas: (1) Point of interest recommendation: this recommendation is for users, including two types of recommendations, one is the destination point, The other is the route recommendation of multiple locations; (2) business location recommendation: this recommendation is mainly for merchants; (3) friend recommendation: online friend recommendation to users. Although scholars have made a great breakthrough in the research of LBSN recommendation system, there are still some problems as follows: (1) the number of interest points and users is large, the amount of computation is large, the data is sparse; (2) the expression method of user relationship is too simple. (3) the cold start problem of the new user (4) not combining the temporal and spatial context information. In view of the above problems, this paper puts forward some new ideas and solutions (1). By dividing urban cells, reducing the size of problem analysis, and solving the problem of partial data sparse; (2) enriching the representation of friends, more in line with the real world. In the general LBSN data, the friend relationship is only 01 (whether there is or not), and obviously does not take into account the intimate density of the friend relationship, this paper will combine the on-line and offline data to quantify the friend relationship. Therefore, it plays a more important role in recommendation system. (3) aiming at the problem of context information, this paper proposes to combine space-time information with user space-time features. Analyze the temporal and spatial characteristics of users and interest points and the current spatiotemporal information to make better recommendations. In addition, this paper proposes a new business location problem and a friend recommendation model based on random walk between users and points of interest. In the experiment, we choose the famous foreign LBSN data set Foursquare for analysis and experiment, and compare the methods and previous research results with some evaluation criteria such as accuracy, recall rate and so on. A series of tests on the algorithm of this paper can realize the effective recommendation to users and POI suppliers.
【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
[Abstract]:With the further development of Internet technology, social networks are becoming more and more popular in people's lives. People use mobile social networks to find useful life information, share life experiences, and interact with friends. And with Android, Apple and other smart mobile devices around 2010, the popularity of applications. Based on the existing social network, a new concept of social network, Location-based Social Network (LBSN), is formed by combining the relevant information of the user's geographical location. The location-based social network not only pays attention to the information published on the user line and the friends relationship on the user line, but also preserves the activity data and the activity pattern below the user line to understand the daily behavior of the user. Because LBSN contains a large number of types of data. We can mine the data and find meaningful information. At present, the research on personalized recommendation based on LBSN data is very hot. Using LBSN data to recommend users is mainly focused on the following three areas: (1) Point of interest recommendation: this recommendation is for users, including two types of recommendations, one is the destination point, The other is the route recommendation of multiple locations; (2) business location recommendation: this recommendation is mainly for merchants; (3) friend recommendation: online friend recommendation to users. Although scholars have made a great breakthrough in the research of LBSN recommendation system, there are still some problems as follows: (1) the number of interest points and users is large, the amount of computation is large, the data is sparse; (2) the expression method of user relationship is too simple. (3) the cold start problem of the new user (4) not combining the temporal and spatial context information. In view of the above problems, this paper puts forward some new ideas and solutions (1). By dividing urban cells, reducing the size of problem analysis, and solving the problem of partial data sparse; (2) enriching the representation of friends, more in line with the real world. In the general LBSN data, the friend relationship is only 01 (whether there is or not), and obviously does not take into account the intimate density of the friend relationship, this paper will combine the on-line and offline data to quantify the friend relationship. Therefore, it plays a more important role in recommendation system. (3) aiming at the problem of context information, this paper proposes to combine space-time information with user space-time features. Analyze the temporal and spatial characteristics of users and interest points and the current spatiotemporal information to make better recommendations. In addition, this paper proposes a new business location problem and a friend recommendation model based on random walk between users and points of interest. In the experiment, we choose the famous foreign LBSN data set Foursquare for analysis and experiment, and compare the methods and previous research results with some evaluation criteria such as accuracy, recall rate and so on. A series of tests on the algorithm of this paper can realize the effective recommendation to users and POI suppliers.
【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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