【摘要】：大數據浪潮下,互聯網在方便人們獲取數據的同時,也將人們置于海量信息選擇的困境中。傳統的搜索引擎雖能過濾掉一部分無關信息,但從大量搜索結果中逐一挑選也是一件費時的事情。學術界和工業(yè)界就這一問題具有一致觀點,即推薦系統是當前緩解大數據災難最為有效的方式。音樂之于人類而言,已經成為生活的重要部分。隨著互聯網服務的發(fā)展,人們對音樂的消費模式也發(fā)生了巨變,以用戶為中心的相關技術已成為目前音樂服務的主流技術。社會化標簽既反映了資源的特征屬性,也體現了用戶的興趣所在。同時,用戶在不同時間對標簽的感興趣程度是在不斷變化的。本文將結合這兩個特性,提出一種基于標簽和時間加權的推薦算法模型。利用用戶行為日志,建立時間遷移下的用戶興趣模型,并通過社會化標簽標識歌曲內容,然后依據用戶興趣模型與備選歌曲的標簽特征的匹配程度來生成推薦列表。同時標簽可以很好地解釋推薦該項目的原因,提高用戶的接受度。為了解決數據稀疏性的問題,本文還加強了對隱式數據的利用。為解決新用戶冷啟動問題,本文提出了一種基于用戶基本信息查找相似用戶的解決方案。首先利用三部圖的物質擴散算法衍生的相似度計算法則,將系統中具有相似行為的用戶進行聚類,再將用戶的基本信息與其類別建立關系,由此便可根據新用戶基本信息,找到其相似用戶群,為其賦予初始標簽偏好特征,從而為新用戶生成個性化推薦列表。最后,本文針對個性化音樂推薦系統的需求設計了推薦系統總體架構,劃分了各功能子模塊,對各模塊進行了詳細設計與實現。在交互模塊,所設計的音樂可視化界面能直觀地展示項目間的關聯性,提高用戶體驗。
[Abstract]:Under the tide of big data, the Internet not only makes it convenient for people to obtain data, but also puts people in the dilemma of mass information choice. Although traditional search engines can filter out some irrelevant information, it is also time-consuming to select one by one from a large number of search results. Academia and industry agree that recommendation system is the most effective way to alleviate big data disaster. Music has become an important part of human life. With the development of Internet service, the consumption mode of music has changed greatly, and the user-centered technology has become the mainstream technology of music service. Social tags not only reflect the characteristics of resources, but also reflect the interests of users. At the same time, the extent to which users are interested in labels is constantly changing at different times. In this paper, a recommendation algorithm model based on label and time weighting is proposed. By using user behavior log, the user interest model under time migration is established, and the content of songs is identified by social tags, and then the recommendation list is generated according to the matching degree between user interest model and label characteristics of alternative songs. At the same time, the label can explain the reason why the project is recommended and improve the acceptance of the user. In order to solve the problem of data sparsity, we also strengthen the use of implicit data. In order to solve the cold start problem of new users, this paper proposes a solution for finding similar users based on user's basic information. Firstly, by using the similarity calculation rule derived from the material diffusion algorithm of the three-part graph, the users with similar behavior in the system are clustered, and then the basic information of the user is related to its category, and then the basic information of the new user can be based on the basic information of the new user. Find the similar user group, assign the initial label preference feature, and generate the personalized recommendation list for the new user. Finally, according to the requirements of the personalized music recommendation system, this paper designs the overall framework of the recommendation system, divides each functional sub-module, and designs and implements each module in detail. In the interactive module, the design of the music visualization interface can show the relationship between projects intuitively and improve the user experience.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2018
【分類號】：TP391.3

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本文編號：2329780