本文選題：農(nóng)資交易　切入點：推薦系統(tǒng)　出處：《河南大學(xué)》2016年碩士論文

【摘要】：為滿足用戶獲得個性化信息的需求,推薦系統(tǒng)得到了快速的發(fā)展,被廣泛應(yīng)用到各種領(lǐng)域中,特別是在電子商務(wù)領(lǐng)域。在電子商務(wù)系統(tǒng)中,利用推薦系統(tǒng)為用戶提供相關(guān)聯(lián)或潛在感興趣的物品,能夠針對不同用戶的需求提供個性化信息服務(wù),在方便用戶獲得信息的同時也提升了用戶的忠實度。在“互聯(lián)網(wǎng)+”的時代背景下,農(nóng)村信息化的進程也開始推進�！靶滦娃r(nóng)村社區(qū)電子商務(wù)與物流信息服務(wù)系統(tǒng)”作為河南省重大專項(新型農(nóng)村社區(qū)信息服務(wù)關(guān)鍵技術(shù)集成與應(yīng)用)建設(shè)的核心內(nèi)容之一,面向各類涉農(nóng)信息,以推薦算法為核心支撐,圍繞供求信息進行匹配,為用戶提供智能的信息撮合服務(wù),促進農(nóng)產(chǎn)品流通、加快農(nóng)業(yè)產(chǎn)業(yè)化進程、促進農(nóng)民增收,具有重要的現(xiàn)實意義�；谖锲返膮f(xié)同過濾推薦算法是電商系統(tǒng)中應(yīng)用較為廣泛的一種算法,該算法是通過用戶對不同物品的評分來評測物品之間的相似度,并基于物品之間的相似度做出推薦。同時,該算法具有不需要領(lǐng)域知識,個性化、自動化程度高,以及隨著時間的推移其性能有所提高等優(yōu)點。但是,在算法的實際應(yīng)用過程中,需要結(jié)合具體應(yīng)用問題進行調(diào)整或改進。具體地:本系統(tǒng)中,在充分考慮物品之間的關(guān)聯(lián)關(guān)系的基礎(chǔ)上,提供靈活的推薦方式。物品關(guān)聯(lián)關(guān)系計算方面:首先創(chuàng)建物品同現(xiàn)矩陣,描述任意兩物品之間關(guān)系;然后通過構(gòu)建物品評分矩陣,用來刻畫物品之間的關(guān)聯(lián)度,為物品之間的相似度計算提供數(shù)據(jù)支撐。推薦方式方面:在系統(tǒng)中提供價格優(yōu)先和距離優(yōu)先兩種推薦方式,當(dāng)選用不同的方式進行推薦時,分別考慮價格及地域兩個屬性構(gòu)建不同的推薦規(guī)則,從而使系統(tǒng)靈活穩(wěn)定的的進行個性化推薦。在分析新型農(nóng)村社區(qū)電子商務(wù)與物流信息服務(wù)系統(tǒng)的實際需求以及技術(shù)路線的基礎(chǔ)上,以基于物品的協(xié)同過濾推薦算法為核心算法,開展了面向農(nóng)資交易的推薦系統(tǒng)的設(shè)計與實現(xiàn)。本文主要工作如下:1.系統(tǒng)需求分析。首先對系統(tǒng)的總體需求進行分析,講述對象模型以及系統(tǒng)數(shù)據(jù)流,在總體需求的基礎(chǔ)上,從推薦系統(tǒng)的業(yè)務(wù)需求以及數(shù)據(jù)需求兩方面細化推薦系統(tǒng)的需求。2.推薦系統(tǒng)架構(gòu)設(shè)計。根據(jù)推薦系統(tǒng)的實際需求,為推薦系統(tǒng)設(shè)計一種穩(wěn)定、高效的架構(gòu)。以推薦系統(tǒng)的業(yè)務(wù)需求與數(shù)據(jù)需求為出發(fā)點合理的設(shè)計數(shù)據(jù)模型,并以設(shè)計的數(shù)據(jù)模型為前提對基于物品的協(xié)同過濾算法進行分析。3.推薦系統(tǒng)設(shè)計與實現(xiàn)。創(chuàng)建物品同現(xiàn)矩陣,描述任意兩個物品之間的關(guān)系。構(gòu)建物品評分矩陣,分析不同用戶對不同物品的評分來評測物品之間的相似性,并計算物品權(quán)值為用戶進行推薦。對于在不同推薦方式下生成不同推薦規(guī)則的算法,使用策略模式的設(shè)計思想進行封裝,使上層調(diào)用者能夠統(tǒng)一的處理數(shù)據(jù)。根據(jù)系統(tǒng)中的數(shù)據(jù)對推薦系統(tǒng)進行驗證。
[Abstract]:In order to meet the needs of users to obtain personalized information, recommendation system has been rapidly developed and widely used in various fields, especially in the field of electronic commerce. Using recommendation systems to provide users with associated or potentially interesting items, they can provide personalized information services tailored to the needs of different users. It not only facilitates users' access to information, but also enhances their loyalty. In the context of the "Internet" era, E-commerce and logistics information service system of new rural community is one of the core contents of Henan Province's major project (key technology integration and application of new rural community information service). For all kinds of agricultural information, with the recommendation algorithm as the core support, matching around the information of supply and demand, providing users with intelligent information matchmaking service, promoting the circulation of agricultural products, speeding up the process of agricultural industrialization, promoting farmers' income, The article based collaborative filtering recommendation algorithm is one of the most widely used algorithms in the e-commerce system. The algorithm evaluates the similarity between the items through the users' scores on different items. At the same time, the algorithm has the advantages of no domain knowledge, individuation, high degree of automation and improved performance over time. In the process of practical application of the algorithm, it is necessary to adjust or improve it in combination with the specific application problem. Specifically, in this system, on the basis of fully considering the relationship between items, To provide flexible recommendation methods. In terms of item correlation calculation, we first create an item co-occurrence matrix to describe the relationship between any two items, and then construct an item score matrix to describe the correlation between items. To provide data support for the similarity calculation between items. Recommendation: in the system to provide price-first and distance-first, when the choice of different ways to recommend, Different recommendation rules are constructed based on price and geographical attributes, On the basis of analyzing the actual demand and technical route of the new rural community electronic commerce and logistics information service system, the article takes the collaborative filtering recommendation algorithm based on articles as the core algorithm. The main work of this paper is as follows: 1. System requirement analysis. Firstly, the overall requirements of the system are analyzed, and the object model and system data flow are described. Detail the requirement of recommendation system from two aspects: the business requirement of recommendation system and the requirement of data. 2. The architecture design of recommendation system. According to the actual requirement of recommendation system, a kind of stability is designed for recommendation system. Efficient architecture. A reasonable design data model based on the business and data requirements of the recommendation system. Based on the designed data model, the collaborative filtering algorithm based on articles is analyzed. 3. The design and implementation of the recommendation system. The article co-occurrence matrix is created, the relationship between any two items is described, and the item scoring matrix is constructed. This paper analyzes the different users' scores on different items to evaluate the similarity between the items, and calculates the weight of the items to be recommended by the user. The design idea of policy pattern is used to encapsulate it so that the upper layer callers can deal with the data uniformly and validate the recommendation system according to the data in the system.
【學(xué)位授予單位】：河南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3

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