【摘要】：電子商務(wù)中產(chǎn)生越來越多的產(chǎn)品和交易信息,使得用戶快速找到自己想要的產(chǎn)品變得越來越困難。同時,電子商務(wù)企業(yè)也面臨著如何推薦讓用戶滿意的產(chǎn)品從而提高銷售量的問題。電子商務(wù)推薦系統(tǒng)就是為了解決這些問題而產(chǎn)生的。協(xié)同過濾這類推薦技術(shù)更多的是基于用戶產(chǎn)生的數(shù)據(jù)直接進行分析,很少基于經(jīng)濟行為來進行電子商務(wù)的推薦。本文結(jié)合經(jīng)濟學(xué)中的總剩余最大化(Total Surplus Maximization)和物品上下文約束,提出了基于總剩余最大化和物品上下文約束的協(xié)同推薦模型。首先,根據(jù)用戶的購買記錄情況,綜合用戶的關(guān)聯(lián)購買和時間局部性,構(gòu)建物品相似度矩陣;其次,通過矩陣分解和物品上下文約束構(gòu)建用戶的個性化效用模型,并根據(jù)最后一單元零剩余法則,構(gòu)造用戶效用的目標函數(shù),使用消費數(shù)據(jù)訓(xùn)練得到用戶的個性化效用預(yù)測模型;最后根據(jù)總剩余最大化模型(TSM),得到用戶對物品的購買預(yù)測模型,使消費者利益和生產(chǎn)者利益總和達到最大。本文通過引入物品的上下文約束,緩解消費記錄矩陣的稀疏問題,更好的預(yù)測用戶的個性化效用,最終得到更好的購買行為預(yù)測。論文在TaFeng超市銷售數(shù)據(jù)集上進行實驗對比和結(jié)果分析。從而得出結(jié)論,我們的模型與過去相關(guān)的協(xié)同過濾和基本的TSM算法相比,在稀疏數(shù)據(jù)集上取得了更好的推薦效果。
[Abstract]:More and more products and transaction information are generated in electronic commerce, which makes it more and more difficult for users to find the products they want quickly. At the same time, e-commerce enterprises are faced with the problem of how to recommend products that satisfy users to increase sales volume. E-commerce recommendation system is to solve these problems. Collaborative filtering is more based on the analysis of user-generated data, and rarely on the basis of economic behavior to recommend e-commerce. In this paper, a collaborative recommendation model based on total residual maximization and article context constraint is proposed, which combines the total surplus maximization (Total Surplus Maximization) and the article context constraint in economics. Firstly, according to the user's purchase record, the article similarity matrix is constructed by synthesizing the associated purchase and time locality of the user. Secondly, the user's personalized utility model is constructed by matrix decomposition and article context constraint, and the objective function of user's utility is constructed according to the zero residue rule of the last unit. The user's personalized utility prediction model is obtained by using consumer data training. Finally, according to the total surplus maximization model (TSM), the prediction model of the consumer's purchase of the goods is obtained, which makes the sum of the consumer's interest and the producer's interest maximum. By introducing the contextual constraints of items, this paper alleviates the problem of sparse consumption record matrix, better predicts the personalized utility of users, and finally obtains a better prediction of purchasing behavior. This paper carries on the experiment contrast and the result analysis on the TaFeng supermarket sales data set. It is concluded that compared with the previous collaborative filtering and basic TSM algorithms, our model achieves better recommendation results on sparse datasets.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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本文編號：2350210