本文選題：低秩矩陣填充 + NMF　； 參考：《小型微型計(jì)算機(jī)系統(tǒng)》2017年12期

【摘要】：針對協(xié)同過濾推薦中評分矩陣極度稀疏問題,以及很多應(yīng)用對數(shù)據(jù)存在非負(fù)約束要求,提出一種結(jié)合矩陣填充及用戶-興趣子集劃分的協(xié)同推薦算法.首先提出非負(fù)約束下的低秩矩陣填充模型(Non-negative Constrained Low Rank Matrix Completion,LR-NM F),以及有效求解該模型的迭代算法.該算法不僅可以利用重構(gòu)矩陣填充原始矩陣中的缺失項(xiàng),而且可以得到評分矩陣的非負(fù)分解表示.在此基礎(chǔ)上,提出一種結(jié)合LR-NMF的基于群組的協(xié)同推薦方法.利用矩陣非負(fù)分解結(jié)果,通過塊模型近似算法劃分用戶-興趣子集或物品-特征子集,最終產(chǎn)生top-N協(xié)同推薦列表.實(shí)驗(yàn)結(jié)果表明,提出的方法不僅有效填充評分矩陣的缺失項(xiàng),而且推薦精度優(yōu)于其它協(xié)同推薦算法.在大規(guī)模稀疏數(shù)據(jù)集中,仍然具有很好的性能.
[Abstract]:Aiming at the problem of extremely sparse score matrix in collaborative filtering recommendation and the non-negative constraint requirement of many applications, a collaborative recommendation algorithm combining matrix filling and user-interest subset partition is proposed. In this paper, a non-negative Constrained Low Rank Matrix completion model with non-negative constraints is proposed, and an iterative algorithm for solving the model is presented. The algorithm can not only fill the missing items in the original matrix with the reconstruction matrix, but also obtain the non-negative decomposition representation of the score matrix. On this basis, a collaborative recommendation method based on LR-NMF is proposed. By using the matrix nonnegative decomposition result, the block model approximation algorithm is used to divide the user-interest subset or the item-feature subset, and finally the top-N collaborative recommendation list is generated. The experimental results show that the proposed method not only fills the missing items in the scoring matrix effectively, but also has better recommendation accuracy than other collaborative recommendation algorithms. In large sparse datasets, it still has good performance.
【作者單位】： 山東師范大學(xué)信息科學(xué)與工程學(xué)院;山東建筑大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61672329,61373149,61472233)資助 山東省科技計(jì)劃項(xiàng)目(2014GGB01617)資助 山東省教育科學(xué)規(guī)劃項(xiàng)目(ZK1437B010)資助 山東省精品課程項(xiàng)目(2012BK294,2013BK399,2013BK402)資助
【分類號】：TP391.3
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本文編號：1820405