【摘要】：隨著互聯(lián)網(wǎng)時代尤其是移動互聯(lián)網(wǎng)時代的到來,當(dāng)代的我們都處在一個“信息爆炸”的時代,由于個性化需求的增強,導(dǎo)致用戶需要個人去過濾掉大量的無效信息,無形中仍然沒有根本性的解決信息量過多的難題。于是,個性化推薦算法出現(xiàn)并成為了大家關(guān)注的熱門話題。個性化推薦系統(tǒng)自出現(xiàn)以來就獲得了廣泛的關(guān)注,眾多的專家學(xué)者都提出了各自關(guān)于個性化推薦系統(tǒng)的研究方法。當(dāng)前主流的推薦算法主要是基于內(nèi)容的推薦算法、基于圖結(jié)構(gòu)的推薦算法、協(xié)同過來推薦算法以及混合推薦算法。但是當(dāng)前的推薦算法由于過分依賴數(shù)據(jù)的顯性評分而存在冷啟動、數(shù)據(jù)稀疏性以及推薦滯后等影響推薦精度的問題。本文對個性化推薦算法的優(yōu)化問題進行了研究,重點對如何充分利用用戶的隱性行為和行業(yè)領(lǐng)域知識為用戶進行更加精準(zhǔn)的個性化推薦進行了深入研究。提出了一種基于用戶需求深度驅(qū)動的個性化推薦算法。算法主要針對當(dāng)前推薦算法存在的冷啟動、數(shù)據(jù)稀疏性、推薦滯后等問題提出了自己的改進方案,在進行用戶聚類時加入了用戶的隱性行為分析,綜合利用用戶隱性行為信息和用戶的屬性信息來實現(xiàn)為用戶進行聚類。同時在為用戶生成推薦列表時,加入了行業(yè)的領(lǐng)域知識,根據(jù)大數(shù)據(jù)來生成行業(yè)鏈,從相似產(chǎn)品的橫向推薦和關(guān)聯(lián)產(chǎn)品的縱向推薦并行推薦實現(xiàn)對用戶進行引導(dǎo)消費,幫助用戶明確潛在需求,產(chǎn)生可觀的經(jīng)濟效益和社會效益。最后將推薦系統(tǒng)設(shè)計為閉環(huán)控制系統(tǒng),由于需求會經(jīng)常發(fā)變化,因此生成推薦列表后系統(tǒng)會在特定時窗檢測推薦精度,可以及時檢測推薦的精確性便于及時調(diào)整推薦列表。本文在進行算法的實驗時采用的是淘寶網(wǎng)舉辦的天池大賽的數(shù)據(jù),通過數(shù)據(jù)集進行了算法的對比驗證,將本文提出的算法與先前的用戶聚類算法與二部圖推薦算法進行了精確性的比對,實驗證明,本文提出的算法在聚類精度和推薦精度方面均有明顯的提升。并通過推薦系統(tǒng)的閉環(huán)設(shè)計,有效的保證了推薦精度的穩(wěn)定性,避免了推薦滯后。
[Abstract]:With the advent of the Internet era, especially the mobile Internet era, we are all in the era of "information explosion". Because of the enhancement of individualized demand, users need individuals to filter out a large number of invalid information. There is still no fundamental solution to the problem of too much information. As a result, personalized recommendation algorithm appeared and became a hot topic. Since the emergence of personalized recommendation system, many experts and scholars have proposed their own research methods of personalized recommendation system. The current mainstream recommendation algorithms are mainly content-based recommendation algorithms, graph structure-based recommendation algorithms, collaborative recommendation algorithms and hybrid recommendation algorithms. However, the current recommendation algorithms have some problems such as cold start, data sparsity and recommendation lag, which affect the recommendation accuracy due to over-reliance on the dominant score of the data. In this paper, the optimization of personalized recommendation algorithm is studied, with emphasis on how to make full use of the implicit behavior of users and industry domain knowledge for users to carry out more accurate personalized recommendation in-depth research. In this paper, a personalized recommendation algorithm based on user's demand depth is proposed. Aiming at the problems of cold start, data sparsity, recommendation lag and so on, the algorithm puts forward its own improvement scheme, and adds the hidden behavior analysis of users in the process of user clustering. The user's hidden behavior information and user's attribute information are used to cluster the user. At the same time, when generating the recommendation list for users, we add the domain knowledge of the industry, according to big data to generate the industry chain, from the horizontal recommendation of similar products and vertical recommendation of related products, we can guide the consumption of users. Help users to identify potential needs, generating considerable economic and social benefits. Finally, the recommendation system is designed as a closed-loop control system. Because the requirements will often change, the system will detect the recommendation accuracy in a specific time window after generating the recommendation list, which can timely detect the recommendation accuracy and facilitate the timely adjustment of recommendation list. This paper uses the data of the Tianchi contest held by Taobao in the experiment of the algorithm, and carries on the comparison and verification of the algorithm through the data set. The proposed algorithm is compared with the previous user clustering algorithm and bipartite graph recommendation algorithm. The experimental results show that the proposed algorithm improves the clustering accuracy and recommendation accuracy obviously. Through the closed-loop design of recommendation system, the stability of recommendation accuracy is effectively guaranteed and the recommendation lag is avoided.
【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】：F274

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