电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2013年
12期
2972-2977
,共6页
移动通信网%信任度%偏好预测%协同过滤
移動通信網%信任度%偏好預測%協同過濾
이동통신망%신임도%편호예측%협동과려
Mobile communication network%Trust degree%Preference prediction%Collaborative filtering
随着移动互联网的发展,人们也对移动互联网的个性化服务提出了更高的要求。为了获取更准确的移动用户偏好以满足个性化服务的要求,该文通过分析移动用户行为,提出一种基于信任度和链接预测的移动用户偏好预测方法。该方法首先通过分析移动用户通信行为,提出一种计算移动用户信任度的方法;然后根据得到的移动用户信任度和移动用户评分相似度选取移动用户的近似邻居;通过链接预测方法计算移动用户和移动网络服务之间的相关度,并根据相关度确定需要预测的移动网络服务集合;最后通过该方法对移动用户偏好进行预测。实验结果表明,该方法的预测精确度优于传统的协同过滤技术,在一定程度上解决了稀疏性问题。
隨著移動互聯網的髮展,人們也對移動互聯網的箇性化服務提齣瞭更高的要求。為瞭穫取更準確的移動用戶偏好以滿足箇性化服務的要求,該文通過分析移動用戶行為,提齣一種基于信任度和鏈接預測的移動用戶偏好預測方法。該方法首先通過分析移動用戶通信行為,提齣一種計算移動用戶信任度的方法;然後根據得到的移動用戶信任度和移動用戶評分相似度選取移動用戶的近似鄰居;通過鏈接預測方法計算移動用戶和移動網絡服務之間的相關度,併根據相關度確定需要預測的移動網絡服務集閤;最後通過該方法對移動用戶偏好進行預測。實驗結果錶明,該方法的預測精確度優于傳統的協同過濾技術,在一定程度上解決瞭稀疏性問題。
수착이동호련망적발전,인문야대이동호련망적개성화복무제출료경고적요구。위료획취경준학적이동용호편호이만족개성화복무적요구,해문통과분석이동용호행위,제출일충기우신임도화련접예측적이동용호편호예측방법。해방법수선통과분석이동용호통신행위,제출일충계산이동용호신임도적방법;연후근거득도적이동용호신임도화이동용호평분상사도선취이동용호적근사린거;통과련접예측방법계산이동용호화이동망락복무지간적상관도,병근거상관도학정수요예측적이동망락복무집합;최후통과해방법대이동용호편호진행예측。실험결과표명,해방법적예측정학도우우전통적협동과려기술,재일정정도상해결료희소성문제。
With the development of mobile network, there is a stricter requirement for the quality of personalized mobile user services. In order to obtain more accurate mobile user preferences, a prediction method of mobile user preferences based on trust and link prediction is proposed by analyzing mobile user behaviors. Firstly, a trust calculation method of mobile user is proposed according to the mobile users’ communication behaviors. Then the nearest neighbors of active user are chosen with the obtained trust and the similarity of the ranking to the mobile network services. Then the relate degree between the mobile user and the mobile network service is obtained by the link prediction, and the mobile network services that need to be predicted can be ascertained by the obtained relate degree. Finally, the experimental results show that the proposed method can obtain more accurate mobile user preferences compared with the traditional collaborative filtering, and it can solve the sparsity issue to some extent.