【摘要】：基于位置的社交網(wǎng)絡(luò)(location-based social networks,LBSN)大為流行之余,也帶來了信息過載問題.好友推薦是所有社交網(wǎng)絡(luò)必須面臨的問題,為了改進(jìn)LBSN中好友推薦的效果,構(gòu)建了考慮用戶交友偏好的好友推薦模型(friends recommendation considering users'preference,UPFR).從興趣相似性、距離和熟識(shí)度三個(gè)屬性刻畫LBSN中的用戶,興趣相似性屬性基于信息熵理論計(jì)算、距離屬性通過樸素貝葉斯推導(dǎo)、熟識(shí)度屬性建立在共同好友的基礎(chǔ)上.在對(duì)三個(gè)屬性進(jìn)行集成時(shí),考慮了用戶的交友偏好,通過目標(biāo)用戶的好友列表確定各屬性的權(quán)重,建立了自適應(yīng)用戶交友偏好的好友推薦算法.通過Foursquare上的數(shù)據(jù)實(shí)驗(yàn)證明該算法能取得較優(yōu)的綜合推薦效果.
[Abstract]:Location-based Social Network (location-based social networks,LBSN) is not only popular, but also brings the problem of information overload. Friend recommendation is a problem that all social networks have to face. In order to improve the effect of friend recommendation in LBSN, a friend recommendation model (friends recommendation considering users'preference,UPFR) considering the preference of users is constructed. This paper describes users in LBSN from three attributes: interest similarity, distance and familiarity. Interest similarity attributes are calculated based on information entropy theory, distance attributes are deduced by naive Bayes, and familiarity attributes are based on mutual friends. In the integration of the three attributes, considering the user's preference for making friends, the weight of each attribute is determined by the friend list of the target user, and a friend recommendation algorithm based on the adaptive user's preference for making friends is established. The experimental results on Foursquare show that the proposed algorithm can achieve better comprehensive recommendation results.
【作者單位】： 合肥工業(yè)大學(xué)管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(71331002) 教育部人文社會(huì)科學(xué)研究規(guī)劃基金項(xiàng)目(15YJA630010) 國(guó)家自然科學(xué)基金面上項(xiàng)目(71571059)~~
【分類號(hào)】：TP391.3

【相似文獻(xiàn)】

相關(guān)期刊論文 前1條

1 胥皇;於志文;封云;周興社;;基于LBSN的個(gè)性化旅游包推薦系統(tǒng)[J];計(jì)算機(jī)與現(xiàn)代化;2014年01期

，

本文編號(hào)：2456729