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      |本期目錄/Table of Contents|

      [1]廖彬,張陶,于炯,等.QPR-NN:一種結合二次多項式回歸與神經網絡的推薦算法[J].西安交通大學學報,2019,53(09):79-87+136.[doi:10.7652/xjtuxb201909011]
       LIAO Bin,ZHANG Tao,YU Jiong,et al.QPR-NN:A New Recommendation Algorithm Combining Quadric Polynomial Regression and Neural Network[J].Journal of Xi'an Jiaotong University,2019,53(09):79-87+136.[doi:10.7652/xjtuxb201909011]
      點擊復制

      QPR-NN:一種結合二次多項式回歸與神經網絡的推薦算法(PDF)

      《西安交通大學學報》[ISSN:0253-987X/CN:61-1069/T]

      卷:
      53
      期數:
      2019年第09期
      頁碼:
      79-87+136
      欄目:
      出版日期:
      2019-09-10

      文章信息/Info

      Title:
      QPR-NN:A New Recommendation Algorithm Combining Quadric Polynomial Regression and Neural Network
      作者:
      廖彬12 張陶34 于炯3 國冰磊3 李敏2 劉炎5
      1.新疆財經大學絲路經濟與管理研究院, 830012, 烏魯木齊; 2.新疆財經大學統計與數據科學學院, 830012, 烏魯木齊; 3.新疆大學信息科學與工程學院, 830012, 烏魯木齊; 4.新疆醫科大學 醫學工程技術學院, 830012, 烏魯木齊; 5.清華大學軟件學院, 100084, 北京
      Author(s):
      LIAO Bin12 ZHANG Tao34 YU Jiong3 GUO Binglei3 LI Min2 LIU Yan5
      1.Institute of Silk Road Economy and Management, Xinjiang University of Finance and Economics, Urumqi 830012, China; 2.College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi 830012, China; 3.School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; 4.Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China; 5.School of Software, Tsinghua University, Beijing 100084, China
      關鍵詞:
      推薦算法 深度學習 特征提取 二次多項式回歸
      Keywords:
      recommendation algorithm deep learning feature extraction quadric polynomial regression
      分類號:
      TP391
      DOI:
      10.7652/xjtuxb201909011
      摘要:
      針對傳統推薦算法不能很好地適應數據高規模及高稀疏性的問題,結合深度學習數據建模的方法,提出了一種結合二次多項式回歸與神經網絡(QPR-NN)的推薦算法。在對已有特征提取方法缺陷分析的基礎上,利用二次多項式回歸模型將用戶對物品的評分數據進行特征提取及降維,充分挖掘了用戶與物品之間的相關性。將特征提取后的數據作為深度學習訓練模型的輸入,增加輸入數據與訓練模型之間的匹配度,并將訓練得到的模型用于推薦評分預測。在MovieLens與Epinions兩組數據集上的實驗結果表明:QPR特征提取方法與QPR-NN推薦算法在平分絕對誤差與均方根誤差評價指標上均優于現有的主流算法,QPR-NN推薦算法可以有效提升推薦準確率。
      Abstract:
      The traditional recommendation algorithms cannot adapt to the problem of high-scale and sparse data.A recommendation algorithm combining quadric polynomial regression and neural network(QPR-NN)is proposed in view of the generality of deep learning data modeling, where quadratic regression is combined with neural network.Following the analysis on the defects in the existing feature extraction methods, the algorithm chooses quadratic regression model to extract feature and reduces the dimensions for the user rating data to fully explore the correlation between the user and item data.The data after feature extraction are taken as the input of deep learning training model to increase the matching degree between the input data and the training model, then this model is used for recommending score prediction.For the two datasets of MovieLens and Epinions, the experimental results show that the QPR feature extraction method and the QPR-NN recommendation algorithm are superior to the existing mainstream algorithms in the evaluation indexes mean absolute error and root mean square error, and effectively improve the recommendation accuracy.

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      備注/Memo

      備注/Memo:
      收稿日期: 2019-02-23。作者簡介: 廖彬(1986—),男,副教授,碩士生導師;痦椖: 國家自然科學基金資助項目(61562078,61462079); 新疆維吾爾自治區自然科學基金資助項目(2016D01B014)。
      更新日期/Last Update: 2019-09-04
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