基于社交團(tuán)體和用戶相似度的信息推薦方法
發(fā)布時間:2018-07-17 06:23
【摘要】:社交網(wǎng)站與電子商務(wù)網(wǎng)站逐步實(shí)現(xiàn)信息共享,電子商務(wù)網(wǎng)站利用社交網(wǎng)站信息可以增強(qiáng)信息推薦的準(zhǔn)確性與可信性。文章提出一種利用社交網(wǎng)站的用戶社交網(wǎng)絡(luò)及博文信息實(shí)現(xiàn)基于社交團(tuán)體和用戶相似度的信息推薦方法,該方法利用CNM算法發(fā)現(xiàn)用戶所處的社會團(tuán)體,通過基于語義信息的文本相似計算方法計算微博文本相似度,最后,在社團(tuán)發(fā)現(xiàn)和文本相似度計算的基礎(chǔ)上計算用戶對項(xiàng)目的預(yù)測評分,實(shí)現(xiàn)信息推薦,并通過線下模擬實(shí)現(xiàn)測試該方法的有效性。
[Abstract]:Social networking website and electronic commerce website realize information sharing step by step, and electronic commerce website can enhance the accuracy and credibility of information recommendation by using social network information. In this paper, we propose a method of information recommendation based on the similarity between social groups and users by using the social network and blog information of social networking sites. The CNM algorithm is used to discover the social groups in which users belong. The text similarity is calculated by semantic information based text similarity calculation method. Finally, based on community discovery and text similarity calculation, users' prediction scores on items are calculated, and information recommendation is realized. The effectiveness of the method is tested by offline simulation.
【作者單位】: 南京工業(yè)大學(xué)經(jīng)濟(jì)與管理學(xué)院;
【基金】:國家自然科學(xué)基金項(xiàng)目“技術(shù)范式轉(zhuǎn)換預(yù)警的理論與方法”的研究成果;項(xiàng)目編號:71473119
【分類號】:G358
本文編號:2129385
[Abstract]:Social networking website and electronic commerce website realize information sharing step by step, and electronic commerce website can enhance the accuracy and credibility of information recommendation by using social network information. In this paper, we propose a method of information recommendation based on the similarity between social groups and users by using the social network and blog information of social networking sites. The CNM algorithm is used to discover the social groups in which users belong. The text similarity is calculated by semantic information based text similarity calculation method. Finally, based on community discovery and text similarity calculation, users' prediction scores on items are calculated, and information recommendation is realized. The effectiveness of the method is tested by offline simulation.
【作者單位】: 南京工業(yè)大學(xué)經(jīng)濟(jì)與管理學(xué)院;
【基金】:國家自然科學(xué)基金項(xiàng)目“技術(shù)范式轉(zhuǎn)換預(yù)警的理論與方法”的研究成果;項(xiàng)目編號:71473119
【分類號】:G358
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