本文選題：信息過載 + 個性化推薦　； 參考：《中北大學》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)產(chǎn)業(yè)的蓬勃發(fā)展,在帶給人們生活巨大便利的同時,也帶來嚴重的信息過載問題�，F(xiàn)如今,個性化推薦已成為解決信息過載問題的重要手段之一,其應(yīng)用也滲透于各個領(lǐng)域中,滲透于人們生活的各個方面。基于情境感知的個性化推薦技術(shù)已成為研究的重點,充分考慮情境信息所起的作用,將用戶和商品的情境信息融入到推薦算法中,可以使得推薦更為有效,使得用戶對推薦結(jié)果更為滿意。本文將傳統(tǒng)推薦算法和情境感知作為研究前提,從實際應(yīng)用的角度考慮,針對當前在各種情境下推薦系統(tǒng)存在的問題,提出一種推薦精度和用戶滿意度較高的改進推薦算法并加以應(yīng)用,本文的主要工作內(nèi)容有:(1)對已有的推薦算法、情境感知理論和情境感知推薦技術(shù)進行了簡單介紹,并對它們的研究現(xiàn)狀和存在問題做出了具體分析。(2)針對目前推薦算法存在推薦精度不高、用戶滿意度低等問題,提出一種基于情境相似的協(xié)同過濾改進推薦算法。該算法依據(jù)物理學點電荷間存在磁力的作用,引入情境因子,構(gòu)建新的用戶-情境-商品模型;然后依據(jù)庫侖定律,在添加磁力概念后,重新定義一個用戶相似度公式;最后根據(jù)新的評分聚合函數(shù)計算得出更為準確的評分預(yù)測,從而進行推薦;最終從理論和實驗上驗證了改進算法的良好性能。(3)在改進推薦算法基礎(chǔ)上,將其應(yīng)用于系統(tǒng)中,對基于情境感知的就餐推薦系統(tǒng)進行了詳細的設(shè)計。首先對設(shè)計就餐推薦系統(tǒng)進行了包括業(yè)務(wù)需求和性能需求在內(nèi)的需求分析;接著對系統(tǒng)功能各模塊進行了詳細的設(shè)計,尤其是核心算法所在推薦模塊的設(shè)計與實現(xiàn);最后將傳統(tǒng)算法下和改進算法下就餐推薦系統(tǒng)的推薦結(jié)果進行了分析對比,從而驗證了改進算法的可行性和科學性。
[Abstract]:With the rapid development of Internet industry, it not only brings great convenience to people's life, but also brings serious information overload problem. Nowadays, personalized recommendation has become one of the important means to solve the problem of information overload, and its application has permeated every field and every aspect of people's life. The personalized recommendation technology based on situational awareness has become the focus of the research. Fully considering the role of situational information and integrating the information of users and commodities into the recommendation algorithm can make the recommendation more effective. Make users more satisfied with the recommended results. In this paper, the traditional recommendation algorithm and situational awareness are taken as the research premise, considering from the perspective of practical application, the problems existing in the current recommendation system in various situations are pointed out. An improved recommendation algorithm with high recommendation accuracy and user satisfaction is proposed and applied. The main work of this paper is to introduce the existing recommendation algorithm, the theory of situational awareness and the technology of situation-aware recommendation. The current research situation and existing problems are analyzed. (2) aiming at the problems of low recommendation accuracy and low user satisfaction, a new collaborative filtering improved recommendation algorithm based on situational similarity is proposed. A new user-situation-commodity model is constructed based on the effect of magnetic force between physical point charges, and then a user similarity formula is redefined according to Coulomb's law after adding the concept of magnetic force. Finally, according to the new score aggregation function to calculate a more accurate score prediction, so as to recommend. Finally, the theoretical and experimental results show that the improved algorithm has good performance. (3) on the basis of the improved recommendation algorithm, it is applied to the system. The repast recommendation system based on situational awareness is designed in detail. First of all, the design of the dining recommendation system including business requirements and performance requirements are analyzed, and then the system function modules are designed in detail, especially the design and implementation of the recommendation module where the core algorithm is. Finally, the recommended results of the traditional algorithm and the improved algorithm are analyzed and compared, which verifies the feasibility and scientific nature of the improved algorithm.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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本文編號：1985119