本文選題：林產(chǎn)品推薦 + 協(xié)同過(guò)濾��； 參考：《東北林業(yè)大學(xué)》2016年碩士論文

【摘要】：林產(chǎn)品以其具有的天然、環(huán)保、綠色的優(yōu)勢(shì),成為了健康產(chǎn)品的主流選擇之一。電子商務(wù)平臺(tái)的不斷發(fā)展帶動(dòng)了林業(yè)產(chǎn)品推廣和銷(xiāo)售。但是隨著用戶和產(chǎn)品規(guī)模的不斷增多,出現(xiàn)了嚴(yán)重的“信息負(fù)載”問(wèn)題,因此個(gè)性化推薦服務(wù)應(yīng)運(yùn)而生。個(gè)性化推薦服務(wù)能夠快速主動(dòng)挖掘潛在的購(gòu)買(mǎi)用戶,幫助用戶快速找到可能感興趣或喜歡的商品,不但可以增加網(wǎng)絡(luò)流量、提升營(yíng)業(yè)收入,同時(shí)還能夠加強(qiáng)用戶對(duì)于網(wǎng)站的忠誠(chéng)度以及用戶體驗(yàn)。電子商務(wù)平臺(tái)對(duì)于產(chǎn)品的推薦大多是基于協(xié)同過(guò)濾推薦算法,該算法是迄今為止應(yīng)用最成功的個(gè)性化推薦算法,被廣泛的應(yīng)用到很多領(lǐng)域中。但是隨著互聯(lián)網(wǎng)的快速普及,使得電商平臺(tái)用戶、產(chǎn)品規(guī)模的不斷擴(kuò)大,協(xié)同過(guò)濾算法遇到嚴(yán)重的數(shù)據(jù)稀疏性問(wèn)題,導(dǎo)致推薦的精度和可擴(kuò)展性都在急劇下降。文中對(duì)協(xié)同過(guò)濾算法進(jìn)行深入學(xué)習(xí)和研究過(guò)后,提出了一種基于Weighted SlopeOne(簡(jiǎn)稱(chēng)WSO)的K-means個(gè)性化林產(chǎn)品推薦算法,該算法首先將WSO算法進(jìn)行產(chǎn)品打分的思想應(yīng)用于高維稀疏用戶-產(chǎn)品評(píng)分矩陣的填充上,然后使用改進(jìn)的K-means算法對(duì)用戶進(jìn)行聚類(lèi)生成用戶類(lèi)簇,最后在每個(gè)類(lèi)簇內(nèi)為目標(biāo)用戶實(shí)現(xiàn)推薦服務(wù)。文中以MovieLens數(shù)據(jù)集為數(shù)據(jù)源進(jìn)行對(duì)比試驗(yàn),經(jīng)仿真表明,文中的算法能夠有效地提升推薦的精度和可擴(kuò)展性。以Apache Mahout為實(shí)驗(yàn)平臺(tái),將文中提出的基于WSO的K-means個(gè)性化林產(chǎn)品推薦算法應(yīng)用于京東商城的林產(chǎn)品購(gòu)買(mǎi)評(píng)分中,實(shí)驗(yàn)結(jié)果表明,文中提出算法的precision、recall、MAE指標(biāo)反應(yīng)良好,適宜將文中提出的算法在林產(chǎn)品貿(mào)易銷(xiāo)售平臺(tái)進(jìn)行大范圍推廣,以提升林產(chǎn)品銷(xiāo)量和用戶忠誠(chéng)度。
[Abstract]:Forest products with its natural, environmental protection, green advantages, has become one of the mainstream choice of health products.The continuous development of e-commerce platform has led to the promotion and sale of forestry products.However, with the increasing of users and products, there is a serious problem of "information load", so personalized recommendation service emerges as the times require.Personalized recommendation services can quickly and actively mine potential buyers, help users quickly find products that may be of interest or interest, and not only increase network traffic, but also increase revenue.At the same time can also enhance the user's loyalty to the site and user experience.The product recommendation of e-commerce platform is mostly based on collaborative filtering recommendation algorithm, which is the most successful personalized recommendation algorithm so far, and has been widely used in many fields.However, with the rapid popularization of the Internet, the users of e-commerce platform and the product scale are expanding, the collaborative filtering algorithm meets with serious data sparsity problem, resulting in a sharp decline in the accuracy and scalability of recommendations.After in-depth study and research on collaborative filtering algorithm, a K-means personalized forest product recommendation algorithm based on Weighted Slope one (short for WSO) is proposed.This algorithm first applies the idea of WSO algorithm to the filling of high-dimensional sparse user-product scoring matrix, and then uses the improved K-means algorithm to cluster users to generate user clusters.Finally, the recommendation service is implemented for the target users in each cluster.In this paper, the MovieLens data set is used as the data source. The simulation results show that the proposed algorithm can effectively improve the accuracy and scalability of the recommendation.On the basis of Apache Mahout, the K-means personalized forest product recommendation algorithm based on WSO is applied to the forest product purchase score of JingDong Mall. The experimental results show that the proposed algorithm has a good response to the index of precisioning all forest products.It is suitable to popularize the proposed algorithm in forest products trading and marketing platform in order to promote forest product sales and customer loyalty.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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