【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的蓬勃發(fā)展和電子商務(wù)的規(guī)模不斷擴(kuò)大,個性化推薦技術(shù)給人們的生活帶來了巨大便利。然而,傳統(tǒng)的推薦算法大多局限于靜態(tài)數(shù)據(jù)和單次推薦場景,忽略了推薦應(yīng)用場景隨時間演化特征和推薦算法有效性的伴隨變化。本文結(jié)合網(wǎng)絡(luò)科學(xué)基本理論,采用二分網(wǎng)絡(luò)刻畫推薦問題,結(jié)合推薦場景動態(tài)演化的特征,建立用戶在線選擇模型,研究在線推薦算法的有效性和在線系統(tǒng)的協(xié)同演化問題,并針對大規(guī)模網(wǎng)絡(luò)壓縮提出了新的辦法。主要內(nèi)容有:1.研究了推薦算法的性能在系統(tǒng)中的長期演化特征。本文設(shè)計了一種用戶選擇模型模擬在線系統(tǒng)與推薦算法協(xié)同演化的過程,系統(tǒng)地檢測了幾種經(jīng)典推薦算法的推薦性能處于在線系統(tǒng)演化下的長期變化情況。研究發(fā)現(xiàn),在系統(tǒng)演化完全依賴推薦算法的情況下,推薦算法的單步推薦性能會逐漸變差。有趣的是,研究還發(fā)現(xiàn)了用戶的隨機(jī)選擇會改善推薦算法的長期性能。當(dāng)系統(tǒng)采用混合推薦算法時,研究發(fā)現(xiàn)算法的最優(yōu)參數(shù)值向著是推薦多樣性改善的方向移動,這表明推薦多樣性的改善對保持長期推薦準(zhǔn)確性很重要。最后在實證中驗證了模型的結(jié)果。本研究為設(shè)計長期有效的推薦算法提供了理論支撐。2.提出了一種層次化的動態(tài)網(wǎng)絡(luò)壓縮算法。本文針對大規(guī)模網(wǎng)絡(luò)壓縮算法存在的問題,提出了一種新的層次化動態(tài)網(wǎng)絡(luò)壓縮算法-HDSLN(Hierarchical Dynamic Summarization of Large Networks),通過網(wǎng)絡(luò)分割,邊的重連和迭代壓縮的方法,將一個大規(guī)模網(wǎng)絡(luò)層次化地壓縮成小規(guī)模網(wǎng)絡(luò),同時盡可能地保留網(wǎng)絡(luò)的原有結(jié)構(gòu)。此外,本文還提出了一種新的基于Super-Net的網(wǎng)絡(luò)重建算法,使得我們可以根據(jù)Super-Net盡可能相似地還原出原網(wǎng)絡(luò)。同時,為了驗證算法的性能,我們采用人工和真實數(shù)據(jù)集對HDSLN算法進(jìn)行了實驗和分析。
[Abstract]:With the rapid development of Internet technology and the expansion of e-commerce, personalized recommendation technology has brought great convenience to people's life. However most of the traditional recommendation algorithms are limited to static data and single recommendation scenarios ignoring the evolution characteristics of recommendation scenarios over time and the validity of recommendation algorithms. Combined with the basic theory of network science, the bipartite network is used to describe the recommendation problem, and the dynamic evolution of recommendation scene is combined to establish the online selection model of users. The effectiveness of online recommendation algorithm and the co-evolution of online system are studied. A new method for large-scale network compression is proposed. The main content is: 1. The long-term evolution characteristics of the performance of the recommendation algorithm in the system are studied. In this paper, we design a user selection model to simulate the collaborative evolution of online systems and recommendation algorithms, and systematically detect the long-term variation of the recommendation performance of several classical recommendation algorithms under the evolution of online systems. It is found that the single-step recommendation performance of the recommendation algorithm will deteriorate gradually when the system evolution is completely dependent on the recommendation algorithm. Interestingly, the study also found that random selection of users improves the long-term performance of recommendation algorithms. When the hybrid recommendation algorithm is used in the system, it is found that the optimal parameter value of the algorithm moves towards the direction of the improvement of the recommendation diversity, which indicates that the improvement of the recommendation diversity is very important to maintain the accuracy of the long-term recommendation. Finally, the results of the model are verified in the empirical analysis. This study provides theoretical support for the design of long-term effective recommendation algorithm. 2. A hierarchical dynamic network compression algorithm is proposed. In this paper, a new hierarchical dynamic network compression algorithm, HDSLN (Hierarchical Dynamic Summarization of Large Networks), is proposed to solve the problems of large scale network compression algorithm, which is based on network segmentation, edge reconnection and iterative compression. A large scale network is hierarchically compressed into a small scale network while preserving the original network structure as much as possible. In addition, a new network reconstruction algorithm based on Super-Net is proposed, which enables us to restore the original network as similar as possible according to Super-Net. At the same time, in order to verify the performance of the algorithm, we use artificial and real data sets to test and analyze the HDSLN algorithm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3

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