本文選題：電商場景 + 商品推薦��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著信息技術(shù)和互聯(lián)網(wǎng)的發(fā)展,信息過載現(xiàn)象使得用戶處理海量數(shù)據(jù)并從中找到有效信息的代價(jià)越來越高,個(gè)性化推薦應(yīng)運(yùn)而生。電子商務(wù)是個(gè)性化推薦鄰域的重要應(yīng)用之一,如何從海量商品集中高效地為用戶推薦個(gè)性化商品成為近年來研究熱點(diǎn)。由于隱式反饋數(shù)據(jù)量大且易獲取,專家學(xué)者逐漸將研究重心從顯式評(píng)分推薦轉(zhuǎn)移到隱式反饋推薦,其中以BPR(Bayesian Personalized Ranking)為主流模型。電商隱式反饋具有數(shù)據(jù)量大、存在固有噪聲及正負(fù)樣本極不均衡等特點(diǎn),且性能要求高,現(xiàn)有模型無法直接應(yīng)用,因此本文從采樣策略和用戶偏好定義兩方面對BPR進(jìn)行改進(jìn),旨在同時(shí)提高商品推薦的精度和效率。本文主要工作包括:1)提出一種基于內(nèi)容的混合采樣策略的BPR改進(jìn)算法現(xiàn)有采樣方式未充分考慮噪聲樣本對模型準(zhǔn)確度和收斂速度的影響,而電商更注重推薦精度和高效性。因此本文研究提出一種基于內(nèi)容的混合采樣策略的BPR改進(jìn)算法。該算法同時(shí)考慮商品對信息值、商品內(nèi)容及用戶潛在偏好三個(gè)因素,選擇高質(zhì)量、高可比及高可信的樣本進(jìn)行模型訓(xùn)練,真實(shí)電商數(shù)據(jù)的實(shí)驗(yàn)結(jié)果表明,其在各項(xiàng)評(píng)估指標(biāo)上都優(yōu)于BPR且比現(xiàn)有采樣改進(jìn)算法能更快收斂。2)提出一種基于內(nèi)容和用戶偏好學(xué)習(xí)的個(gè)性化商品推薦模型由于電商數(shù)據(jù)量大、擁有豐富商品內(nèi)容及用戶特殊網(wǎng)購行為特點(diǎn),現(xiàn)有模型無法準(zhǔn)確刻畫用戶偏好。因此本文研究提出一種基于內(nèi)容和用戶偏好學(xué)習(xí)的個(gè)性化商品推薦模型。模型采用混合采樣策略,同時(shí)考慮用戶潛在偏好、商品內(nèi)容及用戶網(wǎng)購行為特點(diǎn),重定義用戶偏好,并添加相應(yīng)置信度,真實(shí)電商數(shù)據(jù)的實(shí)驗(yàn)結(jié)果表明,其相較現(xiàn)有模型能達(dá)到更高推薦精度且能更穩(wěn)定更快的達(dá)到收斂狀態(tài)。
[Abstract]:With the development of information technology and Internet, the phenomenon of information overload makes it more and more expensive for users to process massive data and find effective information. E-commerce is one of the important applications of personalized recommendation neighborhood. How to recommend personalized products efficiently and efficiently from mass commodities has become a hot topic in recent years. Due to the large amount of implicit feedback data and easy to obtain, experts and scholars gradually shift the focus of research from explicit score recommendation to implicit feedback recommendation, in which BPR(Bayesian Personalized ranking is the mainstream model. The implicit feedback of electricity quotient has the characteristics of large amount of data, inherent noise and extreme imbalance of positive and negative samples, and its performance is very high. The existing model can not be directly applied. Therefore, this paper improves BPR from two aspects: sampling strategy and user preference definition. The aim is to improve the accuracy and efficiency of product recommendation at the same time. The main work of this paper includes: (1) A modified BPR algorithm based on mixed sampling strategy based on content is proposed. The existing sampling methods do not fully consider the influence of noise samples on the accuracy and convergence rate of the model, while the ecoquotient pays more attention to the accuracy and efficiency of recommendation. Therefore, an improved BPR algorithm based on content-based mixed sampling strategy is proposed in this paper. The algorithm takes into account the three factors of commodity information value, commodity content and user's potential preference, and selects high-quality, high-comparable and credible samples for model training. The experimental results of real e-commerce data show that, It is superior to BPR in every evaluation index and can converge faster than the existing sampling improved algorithm. 2) A personalized commodity recommendation model based on content and user preference learning is proposed because of the large amount of e-commerce data. The existing models can not accurately depict user preferences because of their rich commodity content and the characteristics of users' special online shopping behavior. Therefore, this paper proposes a personalized commodity recommendation model based on content and user preference learning. The model adopts mixed sampling strategy, taking into account the potential preferences of users, commodity content and the characteristics of users' online shopping behavior, redefining user preferences and adding the corresponding confidence level. The experimental results of real e-commerce data show that: 1. Compared with the existing model, the proposed model can achieve higher recommendation accuracy and more stable and faster convergence.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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本文編號(hào)：1854012