【摘要】：近年來,隨著互聯(lián)網(wǎng)的發(fā)展,網(wǎng)上購物日漸成為人們生活中必不可少的存在,隨著電子商務網(wǎng)站中用戶和物品數(shù)目的大量增長,如何迅速推薦用戶感興趣的物品已成為急需解決的問題。傳統(tǒng)推薦算法更多的關(guān)注用戶評分,對用戶的上下文信息利用不足,本課題在深入研究已有個性化推薦技術(shù)的基礎上,針對用戶的上下文情景,研究并設計了一種基于上下文的協(xié)同過濾推薦算法。首先,本文闡述了個性化推薦系統(tǒng)中常用的各種推薦技術(shù)尤其是協(xié)同過濾技術(shù),充分了解了協(xié)同過濾算法的原理及分類。詳細介紹了幾種經(jīng)典算法的具體過程,分析了這些算法的優(yōu)缺點。然后介紹了上下文感知推薦系統(tǒng)的相關(guān)技術(shù),最后介紹了常用的推薦算法質(zhì)量評估方法。其次,本文提出了一種含有上下文權(quán)重的上下文相似度計算方法,針對現(xiàn)有上下文引入方法的不足,給出了一種上下文預過濾與上下文建模相結(jié)合的上下文引入方法,并提出了一種基于上下文相似度的Slope One改進算法,進行數(shù)據(jù)預測填充,降低數(shù)據(jù)稀疏性,以降低數(shù)據(jù)稀疏性對算法的不利影響。再次,通過對經(jīng)典協(xié)同過濾推薦算法的研究,發(fā)現(xiàn)傳統(tǒng)的協(xié)同過濾算法中存在的不足,結(jié)合上下文相似度,提出一種基于上下文相似度的協(xié)同過濾推薦算法。相對于傳統(tǒng)的推薦系統(tǒng),利用上下文相似度挖掘用戶興趣偏好更加準確,在很大程度上提高了推薦精度。最后,通過實驗對改進模型及算法的性能進行驗證,并與其它算法進行對比,分析評估結(jié)果,驗證了算法在一定程度上得到較好的推薦預測效果。同時也檢驗了算法在不同數(shù)據(jù)集上的性能,驗證了算法的健壯性,本課題研究可提升協(xié)同過濾推薦算法的效率,對個性化推薦技術(shù)的實踐應用提供了一定的理論及方法支持。
[Abstract]:In recent years, with the development of the Internet, online shopping has become an indispensable existence in people's lives, and with the increase of the number of users and items in e-commerce websites, How to quickly recommend items of interest to users has become an urgent problem. The traditional recommendation algorithm pays more attention to the user's score and makes insufficient use of the user's context information. Based on the in-depth study of the existing personalized recommendation technology, this paper aims at the user's context situation. A context-based collaborative filtering recommendation algorithm is studied and designed. Firstly, this paper describes various recommendation technologies, especially collaborative filtering technologies, which are commonly used in personalized recommendation systems, and fully understands the principle and classification of collaborative filtering algorithms. The process of several classical algorithms is introduced in detail, and the advantages and disadvantages of these algorithms are analyzed. Then it introduces the relevant technology of context-aware recommendation system, and finally introduces the commonly used quality evaluation methods of recommendation algorithms. Secondly, a context similarity calculation method with context weight is proposed. Aiming at the shortcomings of existing context introduction methods, a context introduction method combining context prefiltering and context modeling is proposed. An improved Slope One algorithm based on context similarity is proposed to reduce the data sparsity and reduce the data sparsity in order to reduce the adverse effect of data sparsity on the algorithm. Thirdly, through the research of the classical collaborative filtering recommendation algorithm, the shortcomings of the traditional collaborative filtering algorithm are found. A collaborative filtering recommendation algorithm based on the context similarity is proposed. Compared with the traditional recommendation system, it is more accurate to mine user interest preference by using context similarity, which improves the recommendation accuracy to a great extent. Finally, the performance of the improved model and algorithm is verified by experiments, and compared with other algorithms, the evaluation results are analyzed and the results show that the proposed algorithm has a better prediction effect to a certain extent. At the same time, the performance of the algorithm on different data sets is tested, and the robustness of the algorithm is verified. This research can improve the efficiency of collaborative filtering recommendation algorithm, and provide some theoretical and methodological support for the application of personalized recommendation technology.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.3

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