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融合用戶信任度和相似度的基于核心用戶抽取的魯棒性推薦算法

趙明,  閆寒,  曹高峰,  劉昕鴻

趙明, 閆寒, 曹高峰, 劉昕鴻. 融合用戶信任度和相似度的基于核心用戶抽取的魯棒性推薦算法[J]. 電子與信息學(xué)報(bào), 2019, 41(1): 180-186. doi: 10.11999/JEIT180142
引用本文: 趙明, 閆寒, 曹高峰, 劉昕鴻. 融合用戶信任度和相似度的基于核心用戶抽取的魯棒性推薦算法[J]. 電子與信息學(xué)報(bào), 2019, 41(1): 180-186. doi: 10.11999/JEIT180142
Ming ZHAO, Han YAN, Gaofeng CAO, Xinhong LIU. Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity[J]. Journal of Electronics & Information Technology, 2019, 41(1): 180-186. doi: 10.11999/JEIT180142
Citation: Ming ZHAO, Han YAN, Gaofeng CAO, Xinhong LIU. Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity[J]. Journal of Electronics & Information Technology, 2019, 41(1): 180-186. doi: 10.11999/JEIT180142

融合用戶信任度和相似度的基于核心用戶抽取的魯棒性推薦算法

doi: 10.11999/JEIT180142 cstr: 32379.14.JEIT180142

  • 中南大學(xué)軟件學(xué)院 ??長沙 ??410075

基金項(xiàng)目: 國家自然科學(xué)基金(61572526)，中南大學(xué)研究生創(chuàng)新項(xiàng)目(502211708)
詳細(xì)信息
    作者簡介:

    趙明：男，1957年生，博士后，教授，博士生導(dǎo)師，研究方向?yàn)槿褐歉兄o線傳感器網(wǎng)絡(luò)等

    閆寒：女，1991年生，碩士生，研究方向?yàn)闊o線網(wǎng)絡(luò)和數(shù)據(jù)挖掘等

    曹高峰：男，1991年生，碩士生，研究方向?yàn)橥扑]系統(tǒng)和數(shù)據(jù)挖掘等

    劉昕鴻：男，1995年生，碩士生，研究方向?yàn)闄C(jī)器學(xué)習(xí)和數(shù)據(jù)挖掘等

    通訊作者:

    閆寒　yanhan@csu.edu.cn

  • 中圖分類號: TP391.3

Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity

  • School of Software, Central South University, Changsha 410075, China

Funds: The National Natural Science Foundation of China (61572526), The Central South University Graduate Student Innovation Project (502211708)
  • 摘要:

    推薦系統(tǒng)可以方便地幫助人們做出決策，然而，目前很少有研究考慮到剔除不相關(guān)噪聲用戶的影響，保留少量核心用戶做推薦。該文提出基于信任關(guān)系和興趣相似度的核心用戶抽取的新方法。首先計(jì)算所有用戶對之間的信任度和興趣相似度并且排序，然后根據(jù)用戶在最近鄰列表中出現(xiàn)的頻率和位置權(quán)重兩種策略選擇候選核心用戶集合，最后利用用戶的推薦能力篩選出最終的核心用戶并且做推薦。實(shí)驗(yàn)表明利用核心用戶做推薦的有效性，并且證明了利用20%的核心用戶做推薦，可以達(dá)到超過90%的準(zhǔn)確性，而且利用核心用戶做推薦能很好地抵御托攻擊對推薦系統(tǒng)造成的負(fù)面影響。

    Abstract:

    Recommendation systems can help people make decisions conveniently. However, few studies consider the effect of removing irrelevant noise users and retaining a small number of core users to make recommendations. A new method of core user extraction is proposed based on trust relationship and interest similarity. First, all users trust and interest similarity between pairs are calculated and sorted, then according to the frequency and position weight users travel in the nearest neighbor in the list of two kinds of strategies for the selection of candidate core collection of users. Finally, according to the user’s ability the core users are sieved out. Experimental results show that the core user recommendation effectiveness, and verify that the core of user 20% can reach more than recommended accuracy of 90%, and through the use of core user recommendation the negative effects caused by the attacks on the recommendation system can be resisted.

    • HTML全文

  • 圖  1  基于頻率和位置權(quán)重的核心用戶抽取策略

    圖  2  傳統(tǒng)二部圖與基于核心用戶的二部圖推薦算法原理

    圖  3  不同參數(shù)$\lambda $值對所有用戶平均影響力的影響

    圖  4  不同參數(shù)$\alpha $對推薦結(jié)果的影響

    圖  5  核心用戶分情況

    圖  6  算法召回率對比

    圖  7  攻擊用戶是否存在對推薦結(jié)果召回率的影響

    圖  8  核心用戶在不同階段不同方法的流行度與基準(zhǔn)值的差異

    表  1  融合結(jié)果表

    Au (按照信任度大小排序)Bu (按照相似度大小排序)Cu (按照Pu值大小排序)
    序號用戶ID信任度序號用戶ID相似度序號用戶IDPu ($\alpha $=0.4)
    1user10.7652101user20.9652101user10.70
    2user40.5821302user50.8821302user20.60
    3user30.2124203user10.8124203user50.55
    4user50.2007604user30.7007604user30.35
    5user20.1908555user40.5908555user40.30
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2018-02-02
  • 修回日期:  2018-10-23
  • 網(wǎng)絡(luò)出版日期:  2018-10-29
  • 刊出日期:  2019-01-01

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