【摘要】：Twitter、Sina Micro-blog等社交網(wǎng)絡(luò)應(yīng)用為基于位置的服務(wù)提供了大量的情境信息,如用戶ID(who)、簽到時(shí)間(when)、GPS坐標(biāo)(where)、微博內(nèi)容主題詞(what)和微博內(nèi)容誘因詞(why)等,簡稱5W。它們?yōu)橛脩舻男袨楹推醚芯刻峁┝似鯔C(jī)。該文提出了基于5W動(dòng)態(tài)情境感知信息的W5概率模型,并采用包含情境信息的聯(lián)合概率分布分別從時(shí)間、空間和活動(dòng)等方面挖掘用戶動(dòng)態(tài)行為,用于用戶和位置的預(yù)測。該文實(shí)驗(yàn)基于兩個(gè)數(shù)據(jù)集:Geo-text(GT)和Sina-tweets(ST),在數(shù)據(jù)集上進(jìn)行了用戶預(yù)測(UP)和位置預(yù)測(LP)實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,W5模型在UP和LP兩方面準(zhǔn)確率均高于W4模型。同時(shí),W5模型在時(shí)間誤差和空間距離誤差兩方面也取得了較好的性能。
[Abstract]:Social network applications such as Twitter,Sina Micro-blog provide a lot of situational information for location-based services, such as user ID (who), check-in time (when), GPS coordinate (where), Weibo content theme (what) and Weibo content inducer (why), etc., abbreviated as 5W. They provide an opportunity for the study of users' behavior and preferences. In this paper, a W5 probability model based on 5W dynamic situational perception information is proposed, and the joint probability distribution containing situational information is used to mine the dynamic behavior of users from the aspects of time, space and activity respectively, which can be used to predict the user and location. In this paper, the experiment is based on two datasets: Geo-text (GT) and Sina-tweets (ST),. User prediction (UP) and location prediction (LP) experiments are carried out on the dataset. The experimental results show that the accuracy of W5 model is higher than that of W4 model in both UP and LP. At the same time, the W5 model also achieves good performance in terms of time error and spatial distance error.
【作者單位】： 武漢大學(xué)計(jì)算機(jī)學(xué)院;長江大學(xué)計(jì)算機(jī)科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金(61272109)
【分類號】：TP393.092
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本文編號：2507717