本文選題：上下文 + 張量分解　； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：隨著網(wǎng)絡(luò)信息資源的急速增長,用戶快速且準(zhǔn)確地獲取所需信息變得十分困難。搜索引擎的出現(xiàn)解決了用戶一部分查詢的困難,但是目前該工具實(shí)現(xiàn)不了根據(jù)用戶的需求進(jìn)行推薦的功能。個(gè)性化推薦系統(tǒng)是以用戶的需求為標(biāo)準(zhǔn)。比如,商家可以根據(jù)海量的數(shù)據(jù)挖掘用戶的偏好信息,將潛在客戶挖掘出來進(jìn)而將銷售范圍進(jìn)一步擴(kuò)大,從而擁有更多的消費(fèi)群體。個(gè)性化推薦系統(tǒng)就是根據(jù)不同用戶具有不同興趣點(diǎn)這一個(gè)客觀現(xiàn)象,對用戶進(jìn)行個(gè)性化推薦,使用戶能夠在大量信息中快速選定自己需要的商品,從而在選擇商品的過程中減少不必要的挑選時(shí)間。所以,個(gè)性化推薦系統(tǒng)無論針對用戶還是商家而言都具有實(shí)用性和價(jià)值性。協(xié)同過濾推薦算法是對用戶的行為信息進(jìn)行分析,將與目標(biāo)用戶行為信息相近的用戶查找出來,依據(jù)相近用戶對某些物品的偏好度去衡量目標(biāo)用戶對物品的偏好度,將目標(biāo)用戶對物品的偏好度按照從高到低進(jìn)行排序,最后將結(jié)果反饋給目標(biāo)用戶。基于內(nèi)容的推薦算法實(shí)現(xiàn)的主要原理是:根據(jù)對用戶的特征和項(xiàng)目的特征的有效分析進(jìn)行推薦。常用的項(xiàng)目特征分析建立方法包括:貝葉斯模型,神經(jīng)網(wǎng)絡(luò)模型和空間向量模型。用戶的特征則是根據(jù)用戶偏好的項(xiàng)目信息分析得出的。推薦算法可以有效的提高用戶從瀏覽者身份到購買者身份的轉(zhuǎn)化率,從而提升了銷售能力。盡管這些常用的推薦算法已經(jīng)取得了很大成果,但是僅僅根據(jù)單一的評分?jǐn)?shù)據(jù)來挖掘相似的用戶和物品,得出的推薦效果并不是很理想�，F(xiàn)在很多學(xué)者在個(gè)性化推薦算法中加入了一些上下文屬性信息,比如標(biāo)簽、地點(diǎn)等,用這些上下文屬性信息來改善個(gè)性化推薦的效果。本文在閱讀大量文獻(xiàn)的基礎(chǔ)上,對推薦算法的關(guān)鍵技術(shù)進(jìn)行了研究,根據(jù)已有技術(shù)進(jìn)行了創(chuàng)新型改進(jìn),并通過仿真模擬實(shí)驗(yàn)證明了該方案的可行性和優(yōu)勢性。本文的具體成果如下:(1)將用戶之間共同評價(jià)的項(xiàng)目上下文信息和共同評價(jià)過項(xiàng)目的用戶上下文信息融合到推薦算法當(dāng)中,有效提高了推薦效果的準(zhǔn)確率;(2)提出一種基于上下文感知和張量分解的個(gè)性化推薦算法(CATD),并在Movie lens大規(guī)模真實(shí)數(shù)據(jù)集上進(jìn)行了仿真實(shí)驗(yàn),驗(yàn)證了該算法的有效性。(3)利用核密度估計(jì)技術(shù)以及用戶、項(xiàng)目的上下文屬性信息,分別構(gòu)建用戶和項(xiàng)目的偏好模型,在偏好模型基礎(chǔ)上提出了新的相似度計(jì)算方法,再將相似性度量值高的近鄰進(jìn)行融合;最后結(jié)合一定的推薦方法進(jìn)行用戶和項(xiàng)目間的推薦。
[Abstract]:With the rapid growth of network information resources, it is very difficult for users to obtain the required information quickly and accurately. The appearance of search engine solves the difficulty of querying part of the user, but at present, the tool can not realize the function of recommending according to the user's demand. Personalized recommendation system is based on the needs of users. For example, businesses can mine user preferences based on massive data, mining out potential customers, and further expand the range of sales, so as to have more consumer groups. Personalized recommendation system is based on the objective phenomenon that different users have different points of interest, so that users can quickly choose the products they need in a large amount of information. As a result, in the selection of goods in the process of reducing unnecessary selection time. Therefore, personalized recommendation system is practical and valuable for both users and merchants. Collaborative filtering recommendation algorithm is to analyze the user's behavior information, find out the users who are close to the target user's behavior information, and measure the target user's preference for some items according to the similar user's preference for some items. The target user's preference for the item is sorted from high to low, and the result is fed back to the target user. The main principle of the implementation of content-based recommendation algorithm is to make recommendations based on the effective analysis of the features of the users and the features of the items. The commonly used project feature analysis methods include Bayesian model, neural network model and spatial vector model. The characteristics of the user are analyzed according to the item information of the user preference. The recommendation algorithm can effectively improve the conversion rate from the user's identity to the buyer's identity, thus enhancing the sales ability. Although these commonly used recommendation algorithms have made great achievements, but only based on a single score data to mine similar users and items, the recommended results are not very good. Nowadays, many scholars have added some contextual attribute information, such as label, location, etc., to improve the effect of personalized recommendation. On the basis of reading a large number of literatures, this paper studies the key technologies of the recommendation algorithm, and makes an innovative improvement according to the existing technology. The feasibility and superiority of the scheme are proved by simulation experiments. The concrete results of this paper are as follows: (1) the project context information which is evaluated jointly by users and the user context information that has been evaluated jointly is fused into the recommendation algorithm. (2) A personalized recommendation algorithm based on context-aware and Zhang Liang decomposition is proposed and simulated on a large scale real data set of lens. The validity of the algorithm is verified. (3) based on the kernel density estimation technology and the context attribute information of users and items, the preference models of users and items are constructed, and a new similarity calculation method is proposed based on the preference model. Then the neighbor with high similarity measure is fused, and the user and project are recommended with certain recommendation methods.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 孟祥武;劉樹棟;張玉潔;胡勛;;社會(huì)化推薦系統(tǒng)研究[J];軟件學(xué)報(bào);2015年06期

2 郭磊;馬軍;陳竹敏;姜浩然;;一種結(jié)合推薦對象間關(guān)聯(lián)關(guān)系的社會(huì)化推薦算法[J];計(jì)算機(jī)學(xué)報(bào);2014年01期

3 賈冬艷;張付志;;基于雙重鄰居選取策略的協(xié)同過濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2013年05期

4 陳克寒;韓盼盼;吳健;;基于用戶聚類的異構(gòu)社交網(wǎng)絡(luò)推薦算法[J];計(jì)算機(jī)學(xué)報(bào);2013年02期

5 許海玲;吳瀟;李曉東;閻保平;;互聯(lián)網(wǎng)推薦系統(tǒng)比較研究[J];軟件學(xué)報(bào);2009年02期

6 劉建國;周濤;汪秉宏;;個(gè)性化推薦系統(tǒng)的研究進(jìn)展[J];自然科學(xué)進(jìn)展;2009年01期

7 張光衛(wèi);李德毅;李鵬;康建初;陳桂生;;基于云模型的協(xié)同過濾推薦算法[J];軟件學(xué)報(bào);2007年10期

8 邢春曉;高鳳榮;戰(zhàn)思南;周立柱;;適應(yīng)用戶興趣變化的協(xié)同過濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2007年02期

9 曾春,邢春曉,周立柱;個(gè)性化服務(wù)技術(shù)綜述[J];軟件學(xué)報(bào);2002年10期

相關(guān)碩士學(xué)位論文 前2條

1 李瑞峰;基于GPU的圖書推薦系統(tǒng)研究與實(shí)現(xiàn)[D];浙江大學(xué);2012年

2 朱后坤;關(guān)于推薦系統(tǒng)的統(tǒng)計(jì)預(yù)測研究[D];上海交通大學(xué);2010年

，

本文編號：2007913