本文關(guān)鍵詞： 音樂(lè)推薦 協(xié)同過(guò)濾 Spark 列表推薦 空間模型　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在互聯(lián)網(wǎng)時(shí)代,各類的音樂(lè)網(wǎng)站提供了成千上萬(wàn)的歌曲,滿足了人們對(duì)音樂(lè)的需求,為人們帶來(lái)了極大的方便。但是,數(shù)字音樂(lè)的迅速發(fā)展造成了音樂(lè)信息的過(guò)載,面對(duì)海量的歌曲人們難以快速找到符合自己興趣的音樂(lè)。音樂(lè)推薦系統(tǒng)的出現(xiàn)能夠?yàn)橛脩敉扑]可能喜歡的音樂(lè),幫助用戶快速的發(fā)現(xiàn)或者找到自己想要的歌曲。這種推薦服務(wù)能夠?yàn)橛脩籼峁┝己玫氖褂皿w驗(yàn),帶來(lái)商業(yè)利益,因此音樂(lè)推薦領(lǐng)域也成為工業(yè)界和學(xué)者們重視的研究方向。協(xié)同過(guò)濾算法廣泛應(yīng)用于各領(lǐng)域的推薦系統(tǒng)中,但是存在冷啟動(dòng)和可擴(kuò)展性差的問(wèn)題;傳統(tǒng)的推薦算法沒(méi)有考慮到音樂(lè)行為中特有的連續(xù)性行為和環(huán)境的影響,造成了推薦效果不理想。針對(duì)這些問(wèn)題,本文對(duì)音樂(lè)推薦算法進(jìn)行了深入的研究,對(duì)協(xié)同過(guò)濾算法進(jìn)行了改進(jìn)及基于大數(shù)據(jù)分析平臺(tái)的并行化實(shí)現(xiàn);同時(shí),基于音樂(lè)收聽的特點(diǎn),實(shí)現(xiàn)一種音樂(lè)列表的推薦算法。首先,本文對(duì)于協(xié)同過(guò)濾算法進(jìn)行了研究,針對(duì)基于ALS模型的方法進(jìn)行了改進(jìn),根據(jù)用戶屬性信息對(duì)用戶進(jìn)行聚類分群,提出分群協(xié)同過(guò)濾算法。同時(shí),對(duì)分布式計(jì)算技術(shù)進(jìn)行研究,基于Spark平臺(tái)實(shí)現(xiàn)算法的并行化設(shè)計(jì)與實(shí)現(xiàn)。基于Spark平臺(tái)的分群協(xié)同過(guò)濾算法提高了推薦的準(zhǔn)確性和效率,并解決用戶的冷啟動(dòng)問(wèn)題。然后,本文對(duì)于音樂(lè)收聽具有連續(xù)性且與上下文環(huán)境有關(guān)的特點(diǎn),實(shí)現(xiàn)了一種基于空間模型的音樂(lè)列表推薦算法。通過(guò)將三者之間的概率關(guān)系映射為空間距離,構(gòu)建用戶-歌曲-標(biāo)簽空間模型,實(shí)現(xiàn)對(duì)用戶的個(gè)性化并且連續(xù)的歌曲推薦,滿足用戶當(dāng)前音樂(lè)需求。最后,基于上述兩種推薦技術(shù)本文設(shè)計(jì)了一種多功能音樂(lè)推薦系統(tǒng)�；赟park分布式平臺(tái),從實(shí)際需求出發(fā),實(shí)現(xiàn)對(duì)用戶長(zhǎng)期喜好預(yù)測(cè)的音樂(lè)推薦和當(dāng)前會(huì)話期內(nèi)需求偏好的連續(xù)列表推薦功能。
[Abstract]:In the Internet era, all kinds of music websites provide thousands of songs, to meet the needs of people for music, for people to bring great convenience. The rapid development of digital music has resulted in the overload of music information. In the face of massive songs people can not find music that meets their interests quickly. The emergence of music recommendation system can recommend music that users may like. Help users quickly find or find the song they want. This recommendation service can provide users with a good use of experience, bring business benefits. Therefore, the field of music recommendation has become an important research direction in industry and scholars. Collaborative filtering algorithm is widely used in recommendation systems in various fields, but there are some problems such as cold start and poor scalability. The traditional recommendation algorithm does not take into account the unique continuous behavior and the impact of the environment in the music behavior, resulting in unsatisfactory recommendation effect. In view of these problems, this paper makes a deep research on the music recommendation algorithm. The collaborative filtering algorithm is improved and parallelized based on big data analysis platform. At the same time, based on the characteristics of music listening, a music list recommendation algorithm is implemented. Firstly, the collaborative filtering algorithm is studied, and the method based on ALS model is improved. Cluster collaborative filtering algorithm is proposed according to the user attribute information. At the same time, the distributed computing technology is studied. The parallel design and implementation of the algorithm based on Spark platform. The clustering collaborative filtering algorithm based on Spark platform improves the accuracy and efficiency of recommendation and solves the cold start problem of users. In this paper, a music list recommendation algorithm based on spatial model is implemented for music listening which is continuous and context-dependent. By mapping the probabilistic relationship between the three to spatial distance, this paper proposes a new algorithm for music list recommendation based on spatial model. The user-songs-label space model is constructed to realize the personalized and continuous recommendation of songs to meet the current music needs of users. Finally. This paper designs a multi-function music recommendation system based on the above two recommendation technologies. Based on the Spark distributed platform, it starts from the actual requirements. It implements the function of predicting the long-term preferences of the user and the continuous list recommendation of the demand preference in the current session.
【學(xué)位授予單位】：山東大學(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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