發(fā)布時(shí)間：2018-03-01 00:29

  本文關(guān)鍵詞： 多層聚簇 跨類挖掘 推薦系統(tǒng) 協(xié)同過濾　出處：《小型微型計(jì)算機(jī)系統(tǒng)》2017年04期 　論文類型：期刊論文

【摘要】：大多數(shù)電子商務(wù)系統(tǒng)采用協(xié)同過濾的方法向用戶推薦不同類別的商品.分析發(fā)現(xiàn),相似用戶對(duì)相關(guān)類別商品的喜好程度類似(稱之跨類關(guān)聯(lián)).因此,推薦時(shí)應(yīng)同時(shí)考慮用戶之間和商品之間的關(guān)聯(lián)度.實(shí)際應(yīng)用中,商品類別通常組織成多級(jí)目錄,能夠體現(xiàn)不同商品之間的層次關(guān)系.由于低層類別商品少,而高層類別商品多,因此不同層類別的數(shù)據(jù)稀疏度不同.為緩解現(xiàn)有推薦算法數(shù)據(jù)稀疏問題,本文提出一個(gè)高效多層挖掘算法,挖掘不同類別層次上用戶-商品/類別的關(guān)聯(lián)度.為提高推薦性能,還提出一個(gè)基于層次聚簇的跨類推薦通用框架,此模型擴(kuò)展現(xiàn)有協(xié)同過濾算法.在真實(shí)數(shù)據(jù)集上的實(shí)驗(yàn)表明,本文提出的算法具有較高的準(zhǔn)確率和效率.
[Abstract]:Most e-commerce systems use collaborative filtering to recommend different categories of goods to users. Analysis shows that similar users have similar preferences for related categories of goods (known as cross-class associations). In practical applications, commodity categories are usually organized into multilevel catalogs, which can reflect the hierarchical relationship between different commodities. Therefore, the data sparsity of different classes is different. In order to alleviate the problem of data sparsity in existing recommendation algorithms, this paper proposes an efficient multi-layer mining algorithm to mine the user-commodity / class correlation at different class levels. A general cross-class recommendation framework based on hierarchical clustering is proposed. The model extends the existing collaborative filtering algorithms. Experiments on real data sets show that the proposed algorithm has high accuracy and efficiency.
【作者單位】： 武漢大學(xué)計(jì)算機(jī)學(xué)院;武漢大學(xué)信息管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金青年項(xiàng)目(61303025)資助
【分類號(hào)】：TP391.3

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 湯蓉;唐常杰;徐開闊;左R，

本文編號(hào)：1549645