移動互聯(lián)網(wǎng)中基于上下文信息的用戶偏好提取研究
發(fā)布時間：2019-02-12 23:44
【摘要】：迅猛發(fā)展的移動互聯(lián)網(wǎng)逐漸滲透到了人們生活的每一個角落,給傳統(tǒng)的產(chǎn)業(yè)帶來了前所未有的改造、重構(gòu)和顛覆。憑借著與生俱來的情景感知能力,移動互聯(lián)網(wǎng)逐漸打開了個性化服務(wù)的新局面,這也使普適的計(jì)算日漸乏力,給傳統(tǒng)的用戶偏好提取技術(shù)帶來了新的挑戰(zhàn)。傳統(tǒng)的用戶偏好提取技術(shù),大多致力于研究用戶與項(xiàng)目之間的關(guān)系,而較少考慮上下文信息對用戶偏好的影響。盡管目前也有部分結(jié)合少量上下文因素進(jìn)行個性化服務(wù)的研究,然而這些用戶偏好提取算法考慮的上下文因素較為單一,很難擴(kuò)展到多維的上下文的場景,具有一定的局限性。本課題在這一背景下,重點(diǎn)研究了多維上下文下的用戶偏好提取技術(shù),主要內(nèi)容包括:首先,本課題首先對用戶偏好的研究現(xiàn)狀進(jìn)行了調(diào)研,總結(jié)了用戶偏好的建模方法以及用戶偏好的表示方式,并評估了多維上下文下的用戶偏好提取的解決方案。其次,基于多維上下文因素的場景,提出了多維上下文的用戶偏好模型,該模型可擴(kuò)展,能自適應(yīng)上下文因素的變化,根據(jù)用戶在多維上下文場景進(jìn)行用戶偏好的計(jì)算。在模型的構(gòu)造上,本研究首先從用戶心理層面出發(fā),將用戶偏好分解為長期偏好和短期偏好,針對多維上下文因素,則提出將多維上下文分類為用戶上下文、項(xiàng)目上下文以及環(huán)境上下文。在此基礎(chǔ)上,分析了用戶偏好與各類上下文因素的關(guān)系,構(gòu)建了 CB-MF(Context Bias Matrix Factorization)模型,繼而提出了與之相適應(yīng)的CB-MF算法。本文隨后將提出的模型和算法在LDOS-CoMoDa數(shù)據(jù)集上進(jìn)行了仿真,實(shí)驗(yàn)結(jié)果表明,該模型能相比傳統(tǒng)的MF(MatrixFactorization)算法有較好的性能提升,其均方誤差(Root Mean Square Error,RMSE)相比 MF 算法降低了 10.38%,平均絕對誤差(MeanAbsolute Error,MAE)降低了 11.51%。最后,立足于理論的研究成果,本課題設(shè)計(jì)實(shí)現(xiàn)了基于多維上下文用戶偏好提取的視頻播放平臺。除了播放視頻,該WEB平臺還具有上下文信息采集、用戶偏好提取、推薦生成的功能,同時用戶偏好提取引擎使用了本研究提出的CB-MF算法,充分驗(yàn)證了 CB-MF算法的可行性。本文同時給出了該平臺的系統(tǒng)結(jié)構(gòu)以及各個模塊的實(shí)現(xiàn)細(xì)節(jié)。該平臺對于未來多維上下文與用戶偏好關(guān)系的研究有重要的實(shí)踐意義。
[Abstract]:The rapid development of mobile Internet has gradually penetrated into every corner of people's lives, bringing unprecedented transformation, reconstruction and subversion to traditional industries. With the inherent ability of situational perception, the mobile Internet has gradually opened up a new situation of personalized services, which makes the pervasive computing increasingly weak, and brings new challenges to the traditional technology of user preference extraction. Most of the traditional user preference extraction techniques focus on the relationship between the user and the project without considering the influence of context information on the user preference. Although there are some researches on personalized service based on a few contextual factors these user preference extraction algorithms take into account a single contextual factor which is difficult to be extended to multi-dimensional context scenarios and has some limitations. Under this background, this paper focuses on the technology of user preference extraction in multidimensional context. The main contents are as follows: firstly, this paper investigates the current situation of user preference. The modeling method of user preference and the representation of user preference are summarized, and the solution of user preference extraction under multidimensional context is evaluated. Secondly, based on the scenario of multidimensional context, a user preference model of multidimensional context is proposed. The model is extensible and can adapt to the change of contextual factors. The user preference can be calculated according to the user's preference in multidimensional context. In the construction of the model, the user preference is decomposed into long-term preference and short-term preference from the perspective of user psychology, and the multidimensional context is classified as user context. Project context and environment context. On this basis, the relationship between user preference and various contextual factors is analyzed, the CB-MF (Context Bias Matrix Factorization) model is constructed, and the corresponding CB-MF algorithm is proposed. The proposed model and algorithm are then simulated on the LDOS-CoMoDa dataset. The experimental results show that the proposed model can improve the performance better than the traditional MF (MatrixFactorization) algorithm, and the mean square error (Root Mean Square Error,) of the model is better than that of the traditional MF (MatrixFactorization) algorithm. Compared with the MF algorithm, RMSE reduces 10.38 points and the average absolute error (MeanAbsolute Error,MAE) decreases 11.51%. Finally, based on the theoretical research results, this paper designs and implements a video playback platform based on multidimensional context user preference extraction. In addition to playing video, the WEB platform also has the functions of context information collection, user preference extraction and recommendation generation. Meanwhile, the user preference extraction engine uses the CB-MF algorithm proposed in this study, which fully verifies the feasibility of the CB-MF algorithm. This paper also gives the system structure of the platform and the implementation details of each module. The platform has important practical significance for the future study of multidimensional context and user preference.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3
本文編號：2420917
[Abstract]:The rapid development of mobile Internet has gradually penetrated into every corner of people's lives, bringing unprecedented transformation, reconstruction and subversion to traditional industries. With the inherent ability of situational perception, the mobile Internet has gradually opened up a new situation of personalized services, which makes the pervasive computing increasingly weak, and brings new challenges to the traditional technology of user preference extraction. Most of the traditional user preference extraction techniques focus on the relationship between the user and the project without considering the influence of context information on the user preference. Although there are some researches on personalized service based on a few contextual factors these user preference extraction algorithms take into account a single contextual factor which is difficult to be extended to multi-dimensional context scenarios and has some limitations. Under this background, this paper focuses on the technology of user preference extraction in multidimensional context. The main contents are as follows: firstly, this paper investigates the current situation of user preference. The modeling method of user preference and the representation of user preference are summarized, and the solution of user preference extraction under multidimensional context is evaluated. Secondly, based on the scenario of multidimensional context, a user preference model of multidimensional context is proposed. The model is extensible and can adapt to the change of contextual factors. The user preference can be calculated according to the user's preference in multidimensional context. In the construction of the model, the user preference is decomposed into long-term preference and short-term preference from the perspective of user psychology, and the multidimensional context is classified as user context. Project context and environment context. On this basis, the relationship between user preference and various contextual factors is analyzed, the CB-MF (Context Bias Matrix Factorization) model is constructed, and the corresponding CB-MF algorithm is proposed. The proposed model and algorithm are then simulated on the LDOS-CoMoDa dataset. The experimental results show that the proposed model can improve the performance better than the traditional MF (MatrixFactorization) algorithm, and the mean square error (Root Mean Square Error,) of the model is better than that of the traditional MF (MatrixFactorization) algorithm. Compared with the MF algorithm, RMSE reduces 10.38 points and the average absolute error (MeanAbsolute Error,MAE) decreases 11.51%. Finally, based on the theoretical research results, this paper designs and implements a video playback platform based on multidimensional context user preference extraction. In addition to playing video, the WEB platform also has the functions of context information collection, user preference extraction and recommendation generation. Meanwhile, the user preference extraction engine uses the CB-MF algorithm proposed in this study, which fully verifies the feasibility of the CB-MF algorithm. This paper also gives the system structure of the platform and the implementation details of each module. The platform has important practical significance for the future study of multidimensional context and user preference.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3
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