本文選題：移動(dòng)互聯(lián)網(wǎng) + 個(gè)性化推薦�。� 參考：《貴州大學(xué)》2016年碩士論文

【摘要】：伴隨著移動(dòng)互聯(lián)網(wǎng)的爆炸式增長(zhǎng)與全民社交時(shí)代的到來(lái),人們?cè)诤Ａ啃畔⒅蝎@取有效信息的效率正在下降。面對(duì)用戶(hù)對(duì)自身交友需求不明確、用戶(hù)搜索過(guò)濾條件不夠豐富、用戶(hù)搜索結(jié)果信息過(guò)多等問(wèn)題,在交友過(guò)程中如何簡(jiǎn)潔高效的讓用戶(hù)找到興趣相投的好友成為了社交網(wǎng)絡(luò)的關(guān)鍵問(wèn)題之一。個(gè)性化推薦系統(tǒng)能根據(jù)用戶(hù)的基本信息、用戶(hù)行為與好友信息,將顯性信息與隱性信息相結(jié)合,通過(guò)推薦系統(tǒng)發(fā)現(xiàn)用戶(hù)的興趣點(diǎn),從而引導(dǎo)用戶(hù)發(fā)現(xiàn)自己的交友需求,能極大地降低用戶(hù)獲取有效信息的難度,提高社交網(wǎng)絡(luò)的用戶(hù)交友體驗(yàn)。因此,設(shè)計(jì)一套基于移動(dòng)互聯(lián)網(wǎng)交友的個(gè)性化推薦系統(tǒng)具有重要的理論價(jià)值和實(shí)踐意義。本文針對(duì)目前社交網(wǎng)絡(luò)在好友推薦中存在的問(wèn)題,如冷啟動(dòng)、稀疏矩陣等,充分考慮用戶(hù)對(duì)個(gè)性化推薦系統(tǒng)的需求,結(jié)合基于內(nèi)容過(guò)濾算法和協(xié)同過(guò)濾算法,設(shè)計(jì)并實(shí)現(xiàn)了一種將基于內(nèi)容過(guò)濾算法與協(xié)同過(guò)濾算法進(jìn)行加權(quán)綜合的個(gè)性化推薦系統(tǒng)。本系統(tǒng)首先通過(guò)用戶(hù)數(shù)據(jù)的訓(xùn)練集對(duì)不同權(quán)值比的協(xié)同過(guò)濾與基于內(nèi)容過(guò)濾進(jìn)行多次訓(xùn)練,得出加權(quán)綜合性能最佳時(shí)的權(quán)值比。然后利用TF-IDF算法對(duì)目標(biāo)用戶(hù)的基本信息數(shù)據(jù)進(jìn)行預(yù)處理,確定每個(gè)特征項(xiàng)在基于內(nèi)容過(guò)濾模塊中的權(quán)值,并通過(guò)余弦相似度公式計(jì)算用戶(hù)的相似度,得到基于內(nèi)容過(guò)濾模塊的推薦列表。同時(shí)依據(jù)目標(biāo)用戶(hù)的好友關(guān)系,得到協(xié)同過(guò)濾模塊的推薦列表。最后依據(jù)之前確定的兩算法推薦結(jié)果的權(quán)值比,對(duì)兩算法的推薦列表進(jìn)行加權(quán)綜合,得到最終的綜合推薦列表。本系統(tǒng)在結(jié)構(gòu)上分為服務(wù)器和客戶(hù)端,服務(wù)器采用java環(huán)境開(kāi)發(fā),客戶(hù)端采用iOS平臺(tái)。
[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(xué)位授予單位】：貴州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3
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本文編號(hào)：1832008