本文選題：移動互聯(lián)網(wǎng) + 個性化推薦��； 參考：《貴州大學》2016年碩士論文

【摘要】：伴隨著移動互聯(lián)網(wǎng)的爆炸式增長與全民社交時代的到來,人們在海量信息中獲取有效信息的效率正在下降。面對用戶對自身交友需求不明確、用戶搜索過濾條件不夠豐富、用戶搜索結果信息過多等問題,在交友過程中如何簡潔高效的讓用戶找到興趣相投的好友成為了社交網(wǎng)絡的關鍵問題之一。個性化推薦系統(tǒng)能根據(jù)用戶的基本信息、用戶行為與好友信息,將顯性信息與隱性信息相結合,通過推薦系統(tǒng)發(fā)現(xiàn)用戶的興趣點,從而引導用戶發(fā)現(xiàn)自己的交友需求,能極大地降低用戶獲取有效信息的難度,提高社交網(wǎng)絡的用戶交友體驗。因此,設計一套基于移動互聯(lián)網(wǎng)交友的個性化推薦系統(tǒng)具有重要的理論價值和實踐意義。本文針對目前社交網(wǎng)絡在好友推薦中存在的問題,如冷啟動、稀疏矩陣等,充分考慮用戶對個性化推薦系統(tǒng)的需求,結合基于內(nèi)容過濾算法和協(xié)同過濾算法,設計并實現(xiàn)了一種將基于內(nèi)容過濾算法與協(xié)同過濾算法進行加權綜合的個性化推薦系統(tǒng)。本系統(tǒng)首先通過用戶數(shù)據(jù)的訓練集對不同權值比的協(xié)同過濾與基于內(nèi)容過濾進行多次訓練,得出加權綜合性能最佳時的權值比。然后利用TF-IDF算法對目標用戶的基本信息數(shù)據(jù)進行預處理,確定每個特征項在基于內(nèi)容過濾模塊中的權值,并通過余弦相似度公式計算用戶的相似度,得到基于內(nèi)容過濾模塊的推薦列表。同時依據(jù)目標用戶的好友關系,得到協(xié)同過濾模塊的推薦列表。最后依據(jù)之前確定的兩算法推薦結果的權值比,對兩算法的推薦列表進行加權綜合,得到最終的綜合推薦列表。本系統(tǒng)在結構上分為服務器和客戶端,服務器采用java環(huán)境開發(fā),客戶端采用iOS平臺。
[Abstract]:With the explosive growth of mobile Internet and the arrival of the era of social networking, the efficiency of obtaining effective information in mass information is declining. In the face of the user's unclear need for their own friends, the lack of rich conditions for user search and filtering, and the excessive amount of information about the user's search results, One of the key issues in social networking is how to find friends with similar interests in the process of making friends succinctly and efficiently. According to the basic information of the user, the user behavior and the friend information, the personalized recommendation system can combine the explicit information with the hidden information, and discover the user's interest point through the recommendation system, so as to guide the user to discover his need to make friends. It can greatly reduce the difficulty for users to obtain effective information and improve the experience of social network users making friends. Therefore, it is of great theoretical and practical significance to design a personalized recommendation system based on mobile internet dating. Aiming at the problems existing in friend recommendation of social network, such as cold start, sparse matrix and so on, this paper fully considers the user's demand for personalized recommendation system, and combines the content-based filtering algorithm and collaborative filtering algorithm. A personalized recommendation system based on content filtering and collaborative filtering is designed and implemented. In this system, the cooperative filtering and content-based filtering of different weights and values are trained several times through the training set of user data, and the weight / value ratio is obtained when the weighted synthesis performance is the best. Then the TF-IDF algorithm is used to preprocess the basic information data of the target user to determine the weight of each feature item in the content-based filtering module and to calculate the user similarity by using the cosine similarity formula. Get the list of recommendations based on the content filtering module. At the same time, according to the friend relationship of the target user, the recommendation list of the collaborative filtering module is obtained. Finally, according to the weight / value ratio of the recommended results of the two algorithms, the weighted synthesis of the recommended list of the two algorithms is carried out, and the final comprehensive recommendation list is obtained. The structure of the system is divided into server and client. The server is developed by java environment, and the client adopts iOS platform.
【學位授予單位】：貴州大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.3
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本文編號：1832008