《計算機應用研究》|Application Research of Computers

基于潛在標簽挖掘和細粒度偏好的個性化標簽推薦

Personalized tag recommendation based on potential tag mining and fine-grained preference

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作者 李紅梅,刁興春,曹建軍,張磊,馮欽
機構 1.陸軍工程大學,南京 210007;2.國防科技大學 第六十三研究所,南京 210007
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文章編號 1001-3695(2020)01-007-0034-06
DOI 10.19734/j.issn.1001-3695.2018.05.0498
摘要 為進一步提高個性化標簽推薦性能,針對標簽數據的稀疏性以及傳統方法忽略隱藏在用戶和項目上下文中潛在標簽的缺陷,提出一種基于潛在標簽挖掘和細粒度偏好的個性化標簽推薦方法。首先,提出利用用戶和項目的上下文信息從大量未觀測標簽中挖掘用戶可能感興趣的少量潛在標簽,將標簽重新劃分為正類標簽、潛在標簽和負類標簽三類,進而構建〈用戶,項目〉對標簽的細粒度偏好關系,在緩解標簽稀疏性的同時,提高對標簽偏好關系的表達能力;然后,基于貝葉斯個性化排序優化框架對細粒度偏好關系進行建模,并結合成對交互張量分解對偏好值進行預測,構建細粒度的個性化標簽推薦模型并提出優化算法。對比實驗表明,提出的方法在保證較快收斂速度的前提下,有效地提高了個性化標簽的推薦準確性。
關鍵詞 個性化標簽推薦; 潛在標簽挖掘; 貝葉斯個性化排序; 成對交互張量分解
基金項目 國家自然科學基金資助項目
中國博士后科學基金資助項目
本文URL http://www.oirznw.live/article/01-2020-01-007.html
英文標題 Personalized tag recommendation based on potential tag mining and fine-grained preference
作者英文名 Li Hongmei, Diao Xingchun, Cao Jianjun, Zhang Lei, Feng Qin
機構英文名 1.Army Engineering University,Nanjing 210007,China;2.the 63rd Research Institute,National University of Defense Technology,Nanjing 210007,China
英文摘要 To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference. Firstly, BPR-PITF-P leveraged the context information of both users and got to mine potential and useful tags, and got three kinds of tags: positive tags, potential tags, and negative tags. Based on the above, it translated the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helped alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P modeled the preference relationship based on the optimization criteria of Bayesian personalized ranking, and developed a personalized tag recommendation model followed by optimization algorithm. The comparison results show that this proposed method could improve tag recommendation performance in the premise of guarantee convergence speed.
英文關鍵詞 personalized tag recommendation; potential tag mining; Bayesian personalized ranking; pairwise interaction tensor factorization
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收稿日期 2018/5/8
修回日期 2018/6/25
頁碼 34-39
中圖分類號 TP301.6
文獻標志碼 A
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