本文選題：用戶興趣 + 時(shí)間窗��； 參考：《安徽理工大學(xué)》2017年碩士論文

【摘要】：隨著web 2.0時(shí)代的到來(lái),數(shù)據(jù)量呈指數(shù)式增長(zhǎng)。面對(duì)海量數(shù)據(jù)人們無(wú)法從中迅速找到自己所需資源。為了解決人們?cè)谫Y源檢索和選擇上的問(wèn)題,學(xué)者們提出了推薦系統(tǒng)。在推薦系統(tǒng)中,協(xié)同過(guò)濾算法是如今應(yīng)用最廣泛的推薦算法之一。但在實(shí)踐應(yīng)用上,傳統(tǒng)協(xié)同過(guò)濾算法在推薦時(shí)未考慮到針對(duì)用戶興趣變化及項(xiàng)目自身屬性進(jìn)行推薦;從而影響了推薦的質(zhì)量。為了解決此類問(wèn)題,本文主要針對(duì)用戶興趣及項(xiàng)目屬性這兩方面展開(kāi)了著重研究,提出相關(guān)改進(jìn)與創(chuàng)新如下:1)研究分析現(xiàn)有推薦算法的現(xiàn)狀與不足;并通過(guò)用戶已評(píng)價(jià)項(xiàng)目的屬性及評(píng)分構(gòu)建出用戶興趣模型;有效解決了用戶興趣不易捕捉的問(wèn)題。2)針對(duì)傳統(tǒng)協(xié)同過(guò)濾算法在推薦中忽視了推薦項(xiàng)目自身屬性;因此提出了基于用戶興趣的協(xié)同過(guò)濾算法,該算法結(jié)合了項(xiàng)目自身屬性和用戶興趣模型給出了用戶興趣度,促使推薦過(guò)程中忽視的項(xiàng)目屬性問(wèn)題得以解決。3)考慮到用戶興趣隨時(shí)間變化產(chǎn)生的偏移,引入了艾賓浩斯遺忘定律及滑動(dòng)時(shí)間窗來(lái)體現(xiàn)出用戶的興趣偏移,并對(duì)用戶興趣協(xié)同過(guò)濾算法進(jìn)行了改進(jìn)優(yōu)化。實(shí)驗(yàn)數(shù)據(jù)選用于經(jīng)典的MovieLens數(shù)據(jù)集;對(duì)提出的用戶興趣協(xié)同過(guò)濾算法及改進(jìn)后算法進(jìn)行了實(shí)驗(yàn)。驗(yàn)證結(jié)果表明,我們提出的算法在推薦中有效解決了用戶興趣捕捉及項(xiàng)目冷啟動(dòng)問(wèn)題,推薦的質(zhì)量也得到提升。
[Abstract]:With the arrival of the web 2.0 era, the amount of data increases exponentially. In the face of massive data, people can not quickly find their own resources. In order to solve the problem of resource retrieval and selection, scholars put forward the recommendation system. Collaborative filtering is one of the most widely used recommendation algorithms in recommendation systems. In practice, however, the traditional collaborative filtering algorithm does not take into account the change of user interest and the properties of the item itself, which affects the quality of recommendation. In order to solve this kind of problem, this paper mainly focuses on the user interest and the item attribute, and puts forward the related improvement and innovation as follows: 1) Research and analysis the current situation and the insufficiency of the existing recommendation algorithm; The user interest model is constructed through the attributes and scores of the items evaluated by the user, and the problem of user interest capture is solved effectively. 2) in view of the traditional collaborative filtering algorithm, the attributes of the recommendation items are ignored in the recommendation process. Therefore, a collaborative filtering algorithm based on user interest is proposed, which combines the properties of the project itself and the user interest model to give the user interest degree. In order to solve the problem of item attribute, which is neglected in the recommendation process, this paper introduces the Ibinhaus' law of forgetting and sliding time window to reflect the deviation of user's interest, considering the deviation of user's interest over time. The collaborative filtering algorithm of user interest is improved and optimized. The experimental data are selected from the classical Movie Lens dataset, and the proposed collaborative filtering algorithm and the improved algorithm are tested. The verification results show that the proposed algorithm can effectively solve the problems of user interest capture and project cold start, and improve the quality of recommendation.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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