本文選題：多源個人數(shù)據(jù) + 個人數(shù)據(jù)相關(guān)性��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：在生活中,人們使用大量的服務(wù)來滿足生活,工作,學(xué)習(xí)等各個方面的需求。用戶使用服務(wù)的過程中,會產(chǎn)生大量個人相關(guān)的數(shù)據(jù),這些個人數(shù)據(jù)刻畫了個人在不同方面的偏好、習(xí)慣、興趣。雖然這些個人數(shù)據(jù)分散在各個服務(wù)中,但是這些數(shù)據(jù)之間存在以用戶為中心的潛在關(guān)聯(lián),這種相關(guān)性有助于在服務(wù)推薦中為用戶提供更準(zhǔn)確的推薦。現(xiàn)有的推薦算法很少有將用戶的多源數(shù)據(jù)應(yīng)用到算法當(dāng)中。針對如上問題,本文將用戶分散在各個服務(wù)中的數(shù)據(jù)融合在一起,根據(jù)服務(wù)數(shù)據(jù)間的相關(guān)性等信息,為用戶提供更加準(zhǔn)確的推薦。本文主要研究并解決了以下幾個問題:(1)相關(guān)性度量:收集同一活躍用戶在多個服務(wù)中的數(shù)據(jù),基于LDA方法從多源個人數(shù)據(jù)中抽取主題,提出了基于主題相似性的多源個人數(shù)據(jù)相關(guān)性度量方法,進(jìn)而挖掘多源數(shù)據(jù)之間的相關(guān)性呈現(xiàn)的多種相關(guān)性形態(tài)。研究表明即使用戶在不同服務(wù)中產(chǎn)生的個人數(shù)據(jù)是相關(guān)的,相關(guān)性的形態(tài)也不一定相同。(2)相關(guān)性及其形態(tài)演化分析:從時間的角度,分析多服務(wù)個人數(shù)據(jù)之間的相關(guān)性演化遵循的規(guī)律。個人數(shù)據(jù)不是靜態(tài)的,可能隨時間,用戶的經(jīng)歷,興趣愛好等因素而產(chǎn)生變化,所以導(dǎo)致多服務(wù)的個人數(shù)據(jù)之間的相關(guān)性可能不是一成不變的,存在不同演化規(guī)律。因?yàn)橛脩舻男袨榱?xí)慣不同,個體差異很大,導(dǎo)致其相關(guān)性形態(tài)可能存在差異,通過度量相關(guān)性形態(tài)之間的差異,分析用戶相關(guān)性形態(tài)隨著時間變化的演化規(guī)律。(3)推薦策略制定:根據(jù)服務(wù)數(shù)目的不同和服務(wù)數(shù)據(jù)間相關(guān)性信息,制定不同的數(shù)據(jù)融合策略,針對用戶各個服務(wù)間的相關(guān)性,用戶在服務(wù)中的活躍程度等信息,計(jì)算使用不同數(shù)據(jù)融合策略所進(jìn)行推薦的效果存在的差異,得到相關(guān)性對推薦的準(zhǔn)確性的影響。找到使用同一組數(shù)據(jù)做推薦時,對應(yīng)的最優(yōu)的推薦策略。根據(jù)用戶服務(wù)數(shù)據(jù)間的相關(guān)性和推薦對應(yīng)的最優(yōu)策略等信息,為符合某種特征的用戶找到最適合的推薦策略。本文收集了基于共同用戶的多源個人數(shù)據(jù),提出了一種基于主題相似性的多源個人數(shù)據(jù)相關(guān)性度量方法。進(jìn)而,挖掘出若干種典型的個人數(shù)據(jù)相關(guān)性形態(tài),并分析了多源個人數(shù)據(jù)之間的相關(guān)性形態(tài)隨時間演化遵循的規(guī)律。制定六種服務(wù)數(shù)據(jù)融合策略,根據(jù)這些策略在服務(wù)推薦算法中分別融合不同的個人數(shù)據(jù),為用戶制定更加準(zhǔn)確的推薦策略提供了幫助,提升推薦的性能。
[Abstract]:In life, people use a large number of services to meet the needs of life, work, learning and other aspects. In the process of using the service, users will produce a large number of personal related data, which describe the preferences, habits and interests of individuals in different aspects. Although these personal data are scattered in various services, there is a potential user-centric correlation between these data, which helps to provide more accurate recommendation for users in service recommendation. The existing recommendation algorithms rarely apply the user's multi-source data to the algorithm. In order to solve the above problem, the data scattered by users in each service are fused together in this paper, and more accurate recommendation is provided according to the information such as the correlation between the service data and so on. This paper mainly studies and solves the following problems: collecting the data of the same active user in multiple services and extracting the topic from the multi-source personal data based on the LDA method. A multi-source personal data correlation measurement method based on topic similarity is proposed to mine the correlation patterns of multi-source data. Studies have shown that even if the personal data generated by users in different services are relevant, the form of correlation is not necessarily the same. This paper analyzes the rules followed by the evolution of correlation between multi-service personal data. Personal data is not static, it may change with time, user's experience, interest and so on, so the correlation between personal data that leads to multi-service may not be fixed, and there are different evolution rules. Because the user's behavior habit is different, the individual is very different, which may lead to the difference of the correlation form, by measuring the difference between the correlation forms, According to the different number of services and the correlation information between the service data, different data fusion strategies are formulated, aiming at the correlation between different services. Based on the information of the user's activity in the service, this paper calculates the difference in the effect of recommendation with different data fusion strategies, and obtains the effect of correlation on the accuracy of recommendation. Find the optimal recommendation strategy when using the same set of data for recommendation. According to the information of the correlation between user service data and the recommendation corresponding to the optimal policy, the most suitable recommendation strategy is found for the users who conform to certain characteristics. In this paper, we collect multi-source personal data based on common users, and propose a method to measure the correlation of multi-source personal data based on topic similarity. Furthermore, several typical patterns of personal data correlation are mined, and the rules followed by the evolution of multi-source personal data over time are analyzed. Six kinds of service data fusion strategies are formulated according to which different personal data are fused in the service recommendation algorithm which can help users to formulate more accurate recommendation strategies and improve the performance of recommendation.
【學(xué)位授予單位】：哈爾濱工業(yè)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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本文編號：1856224