發(fā)布時間：2018-04-05 13:04

  本文選題：上下文感知推薦　切入點：復雜分割　出處：《江西理工大學》2016年碩士論文

【摘要】：個性化推薦系統(tǒng)的目的是解決信息過載問題,目前已被廣泛應(yīng)用于互聯(lián)網(wǎng)的各個領(lǐng)域。傳統(tǒng)的推薦系統(tǒng)只通過分析用戶-項目之間的二元關(guān)系來為用戶提供推薦,而忽略了上下文信息對用戶決策的影響。隨著上下文感知技術(shù)以及智能移動終端技術(shù)的快速發(fā)展,將上下文感知技術(shù)融入推薦過程的上下文感知推薦系統(tǒng)研究愈演愈烈。該研究在信息檢索、移動互聯(lián)網(wǎng)、物聯(lián)網(wǎng)、電子商務(wù)、智能家居/辦公/交通等諸多工業(yè)領(lǐng)域具有廣泛的應(yīng)用前景。目前該領(lǐng)域的研究在上下文信息挖掘與檢測、用戶建模與行為分析、上下文用戶偏好提取、上下文感知推薦算法等方面都存在許多問題亟待解決。為了進一步提高推薦準確度和效率,本文針對上下文建模方法、推薦生成方法等關(guān)鍵問題進行研究,主要取得了以下成果:(1)為了得到更加專一化的數(shù)據(jù)以進一步提高推薦結(jié)果的準確性,本文提出基于離散二進制粒子群算法的上下文復雜分割方法,將歷史數(shù)據(jù)中處于不同上下文環(huán)境下的同一個用戶(或項目)分割成兩個不同的用戶(或項目)。該方法主要過程為首先利用離散二進制粒子群算法對最佳分割上下文條件組合進行優(yōu)化,然后根據(jù)最佳分割組合中的這些上下文條件對項目或用戶進行分割,便能得到更加專一化的評分數(shù)據(jù),最后將這些數(shù)據(jù)輸入到推薦算法中獲得更加準確的推薦結(jié)果。采用真實電影評分數(shù)據(jù)集進行實驗,得出的結(jié)果驗證了提出算法的有效性和可靠性。(2)針對現(xiàn)有相關(guān)研究存在同等對待所有上下文而忽略各上下文對用戶評分影響力強弱的問題,本文提出基于貝葉斯方法與聚類的上下文用戶興趣建模方法。首先采用特征聚類方法對項目進行聚類,然后利用貝葉斯公式計算單個上下文條件下一個用戶喜歡某類項目的概率,再通過復合概率公式求得多個上下文條件下用戶喜歡一類項目的聯(lián)合概率。最后根據(jù)喜歡同一類項目的用戶之間相似度更高這一認識,將所求的聯(lián)合概率融入到傳統(tǒng)協(xié)同過濾算法中用戶相似度計算過程以提高相似度精度。采用真實電影評分數(shù)據(jù)集進行對比實驗,實驗結(jié)果表明該方法與傳統(tǒng)協(xié)同過濾方法相比能夠有效利用上下文信息提高推薦準確度。
[Abstract]:The purpose of personalized recommendation system is to solve the problem of information overload, which has been widely used in various fields of the Internet.Traditional recommendation systems only analyze the binary relationship between users and items to provide recommendations for users, but ignore the impact of context information on user decisions.With the rapid development of context-aware technology and intelligent mobile terminal technology, the research of context-aware recommendation system which integrates context-aware technology into the recommendation process is becoming more and more serious.This research has a wide range of applications in information retrieval, mobile Internet, Internet of things, e-commerce, smart home / office / transportation and many other industrial fields.At present, there are many problems in this field, such as context information mining and detection, user modeling and behavior analysis, context user preference extraction, context-aware recommendation algorithm and so on.In order to further improve the accuracy and efficiency of recommendation, this paper focuses on some key problems, such as context modeling method, recommendation generation method and so on.In order to obtain more specific data to further improve the accuracy of the recommended results, this paper proposes a context complex segmentation method based on discrete binary particle swarm optimization (Dbinary Particle Swarm Optimization).The same user (or item) in a different context in the historical data is split into two different users (or items).The main process of this method is to first optimize the optimal segmentation context conditions by using discrete binary particle swarm optimization algorithm, and then segment the items or users according to these context conditions in the optimal segmentation combination.Then we can get more specialized scoring data and input these data into the recommendation algorithm to obtain more accurate recommendation results.The experimental results show that the proposed algorithm is effective and reliable. (2) there is a problem that all contexts are treated equally and the influence of each context is ignored.This paper presents a contextual user interest modeling method based on Bayesian method and clustering.Firstly, the feature clustering method is used to cluster the items, and then the Bayesian formula is used to calculate the probability of a user liking a certain type of item under a single context.Then the joint probability of the user like a class of items under multiple contexts is obtained by the compound probability formula.Finally, according to the higher similarity between users who like the same type of items, the proposed joint probability is integrated into the traditional collaborative filtering algorithm to calculate the user similarity to improve the accuracy of similarity.The experimental results show that the proposed method can effectively improve the accuracy of recommendation by using context information compared with the traditional collaborative filtering method.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.3

【參考文獻】

相關(guān)期刊論文 前4條

1 汪從梅;王成良;徐玲;;自適應(yīng)用戶的Item-based協(xié)同過濾推薦算法[J];計算機應(yīng)用研究;2013年12期

2 程飛;賈彩燕;;一種基于用戶相似性的協(xié)同過濾推薦算法[J];計算機工程與科學;2013年05期

3 徐風苓;孟祥武;王立才;;基于移動用戶上下文相似度的協(xié)同過濾推薦算法[J];電子與信息學報;2011年11期

4 鄧愛林,朱揚勇,施伯樂;基于項目評分預測的協(xié)同過濾推薦算法[J];軟件學報;2003年09期

，

本文編號：1714861