本文選題：電子商務(wù) + 商品推薦　； 參考：《山東大學學報(理學版)》2016年01期

【摘要】：數(shù)據(jù)稀疏和運行速度慢是個性化推薦系統(tǒng)面臨的難題。為了有效利用用戶歷史行為,基于用戶的評分記錄識別出用戶感興趣的內(nèi)容,并結(jié)合用戶間的信任關(guān)系,提出使用因子分解機(factorization machine,FM)模型進行評分預(yù)測。FM具有線性時間復(fù)雜度,并且對于稀疏的數(shù)據(jù)具有很好的學習能力,因而能進行快速推薦。試驗結(jié)果表明,與傳統(tǒng)方法相比,基于因子分解機的商品推薦方法的準確度有明顯提高。
[Abstract]:Sparse data and slow running speed are the problems faced by personalized recommendation system. In order to utilize user's historical behavior effectively, the content of user's interest is identified based on the user's score record, and combining with the trust relationship between users, it is proposed that the factorization machine factorization (factorization machine FM) model is used to predict the score of .FM with linear time complexity. And the sparse data has the very good learning ability, therefore can carry on the fast recommendation. The experimental results show that the accuracy of the commodity recommendation method based on factor decomposition machine is obviously improved compared with the traditional method.
【作者單位】： 河池學院計算機與信息工程學院;武漢大學計算機學院;
【基金】：廣西高�？茖W技術(shù)研究項目(KY2015LX338)
【分類號】：TP391.3
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本文編號：2016165