發(fā)布時(shí)間：2018-09-15 06:29

【摘要】：伴隨著近幾年網(wǎng)絡(luò)技術(shù)的不斷普及發(fā)展，信息在互聯(lián)網(wǎng)上的增長(zhǎng)呈現(xiàn)爆炸式的速度，如此龐大的信息給用戶帶來(lái)了檢索的困難，傳統(tǒng)的搜索引擎技術(shù)難以滿足用戶的需求。在這種現(xiàn)有技術(shù)無(wú)法滿足需要的背景下，產(chǎn)生了協(xié)同過(guò)濾系統(tǒng)，進(jìn)而發(fā)展成為推薦領(lǐng)城的科研熱點(diǎn)。 協(xié)同過(guò)濾算法主要分為基于內(nèi)存的協(xié)同過(guò)濾算法和基于模型的協(xié)同過(guò)濾算法兩大類。它是一種基于一系列興趣相同的用戶或項(xiàng)目進(jìn)行的推薦，它參照相鄰用戶的偏好信息產(chǎn)生對(duì)目標(biāo)用戶的推薦清單。本文在比較了兩者在數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果后選擇采用基于項(xiàng)目的改正的余弦相似性算法作為我們音樂(lè)推薦系統(tǒng)的協(xié)同過(guò)濾算法。 該算法在一定程度上彌補(bǔ)了傳統(tǒng)推薦方式的不足，同時(shí)本文結(jié)合協(xié)同過(guò)濾推薦算法帶來(lái)的一定優(yōu)勢(shì)對(duì)現(xiàn)有的音樂(lè)推薦系統(tǒng)做了詳細(xì)的分析，最后采用B/S軟件結(jié)構(gòu)實(shí)現(xiàn)了整個(gè)個(gè)性化音樂(lè)推薦系統(tǒng)的軟件原型。 本文對(duì)音樂(lè)推薦原型系統(tǒng)進(jìn)行了用戶滿意度的評(píng)測(cè)，并且對(duì)系統(tǒng)的可用性進(jìn)行了測(cè)試。通過(guò)評(píng)估推薦結(jié)果我們發(fā)現(xiàn)該系統(tǒng)的整體性能較好，能夠在一定程度上有效地推薦出用戶可能感興趣的結(jié)果，基本上達(dá)到了系統(tǒng)設(shè)計(jì)的預(yù)期目標(biāo)。
[Abstract]:With the continuous development of network technology in recent years, the growth of information on the Internet presents an explosive speed, such a huge amount of information to users have brought difficulties in retrieval, the traditional search engine technology is difficult to meet the needs of users. Under the background that the existing technology can not meet the needs, collaborative filtering system has developed into a hot research spot in the recommended city. Collaborative filtering algorithms are divided into two categories: memory-based collaborative filtering and model-based collaborative filtering. It is a kind of recommendation based on a series of users or items of the same interest. It generates a list of recommendations to target users according to the preference information of neighboring users. In this paper, after comparing the experimental results of the two data sets, we choose the item-based correction cosine similarity algorithm as the collaborative filtering algorithm for our music recommendation system. To some extent, this algorithm makes up for the shortcomings of the traditional recommendation methods. At the same time, this paper makes a detailed analysis of the existing music recommendation system by combining the advantages of the collaborative filtering recommendation algorithm. Finally, the software prototype of the individual music recommendation system is realized by using the B / S software structure. In this paper, the user satisfaction of the music recommendation prototype system is evaluated, and the usability of the system is tested. Through the evaluation of the recommended results we find that the overall performance of the system is good and can effectively recommend the results that the users may be interested in to a certain extent and basically reach the expected goal of the system design.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 趙亮,胡乃靜,張守志;個(gè)性化推薦算法設(shè)計(jì)[J];計(jì)算機(jī)研究與發(fā)展;2002年08期

2 余力,劉魯;電子商務(wù)個(gè)性化推薦研究[J];計(jì)算機(jī)集成制造系統(tǒng);2004年10期

3 吳顏;沈潔;顧天竺;陳曉紅;李慧;張舒;;協(xié)同過(guò)濾推薦系統(tǒng)中數(shù)據(jù)稀疏問(wèn)題的解決[J];計(jì)算機(jī)應(yīng)用研究;2007年06期

4 鄧愛(ài)林,朱揚(yáng)勇,施伯樂(lè);基于項(xiàng)目評(píng)分預(yù)測(cè)的協(xié)同過(guò)濾推薦算法[J];軟件學(xué)報(bào);2003年09期

5 徐蘭芳;胡懷飛;桑子夏;徐鳳鳴;鄒德清;;基于灰色系統(tǒng)理論的信譽(yù)報(bào)告機(jī)制[J];軟件學(xué)報(bào);2007年07期

6 馬宏偉;張光衛(wèi);李鵬;;協(xié)同過(guò)濾推薦算法綜述[J];小型微型計(jì)算機(jī)系統(tǒng);2009年07期

相關(guān)博士學(xué)位論文 前2條

1 郭艷紅;推薦系統(tǒng)的協(xié)同過(guò)濾算法與應(yīng)用研究[D];大連理工大學(xué);2008年

2 李濤;推薦系統(tǒng)中若干關(guān)鍵問(wèn)題研究[D];南京航空航天大學(xué);2009年

，

本文編號(hào)：2244055