本文選題：推薦算法 + 深度學(xué)習(xí)��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的快速發(fā)展,商品種類變得日益繁多,這導(dǎo)致用戶不可能通過逐一瀏覽商品的信息的方式來快速挑選出最滿意的或適合自己的商品,為提高選購效率,推薦算法應(yīng)運(yùn)而生,其作為一類與實(shí)際生活息息相關(guān)的機(jī)器學(xué)習(xí)算法,給用戶帶來便利的同時(shí)也給商家也帶來了巨大的利益,可以稱得上是"雙贏"的算法.深度學(xué)習(xí)是近幾年受到學(xué)者們廣泛關(guān)注的一個(gè)研究領(lǐng)域,由于其深層架構(gòu)的特性使得深度學(xué)習(xí)模型可以學(xué)習(xí)更復(fù)雜的結(jié)構(gòu),所以在語音識(shí)別,機(jī)器翻譯,圖像識(shí)別等領(lǐng)域深度學(xué)習(xí)算法均取得了令人矚目的成果.本文將深度學(xué)習(xí)算法和推薦算法結(jié)合在一起進(jìn)行研究,將深度學(xué)習(xí)中的門限循環(huán)單元模型(gated recurrent unit,GRU)應(yīng)用于推薦算法中,并針對(duì)國外電影評(píng)分集—MovieLens數(shù)據(jù)集和自行爬取的國內(nèi)電影評(píng)分?jǐn)?shù)據(jù)集—豆瓣電影評(píng)分?jǐn)?shù)據(jù)集應(yīng)用該算法進(jìn)行實(shí)際數(shù)據(jù)分析,將此算法與推薦領(lǐng)域其他主流算法進(jìn)行比較,考察其優(yōu)劣.
[Abstract]:With the rapid development of Internet technology, the variety of goods is becoming more and more diverse, which makes it impossible for users to quickly select the most satisfied or suitable products by browsing the information of the products one by one, in order to improve the efficiency of shopping. Recommendation algorithm emerges as the times require. As a kind of machine learning algorithm which is closely related to real life, it brings convenience to users and also brings huge benefits to merchants, which can be called "win-win" algorithm. In recent years, deep learning is a research field that has been widely concerned by scholars. Because of its deep structure characteristics, the deep learning model can learn more complex structure, so it can be used in speech recognition, machine translation. The depth learning algorithms in image recognition and other fields have achieved remarkable results. In this paper, the depth learning algorithm is combined with the recommendation algorithm, and the threshold recurrent unit model in depth learning is applied to the recommendation algorithm. This algorithm is used to analyze the actual data of the foreign film scoring set -Movie Lens dataset and the domestic film score data set which is crawled by ourselves. The algorithm is compared with other mainstream algorithms in the field of recommendation. Examine its merits and demerits.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：J943

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