需要金幣： 1000 個金幣 |
資料包括：完整論文 |  |
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| 轉(zhuǎn)換比率：金額 X 10=金幣數(shù)量， 例100元=1000金幣 | 論文字?jǐn)?shù)：9330 |  | |
| 折扣與優(yōu)惠：團(tuán)購最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要：隨著電子商務(wù)的發(fā)展及應(yīng)用，互聯(lián)網(wǎng)上陳列了越來越多的商品信息，但是僅有極少部分為用戶所關(guān)注，如何為用戶準(zhǔn)確有效地篩選信息成為了電子商務(wù)領(lǐng)域的一大熱點問題。也因此，推薦系統(tǒng)的研究工作備受關(guān)注。協(xié)同過濾算法在推薦系統(tǒng)中有很重要的應(yīng)用, 它的性能直接影響了推薦系統(tǒng)的工作效率。隨著推薦系統(tǒng)的增大，數(shù)據(jù)規(guī)模與稀疏性越來越大，如何減少由于數(shù)據(jù)數(shù)據(jù)稀疏度所帶來的影響，成為近些年來協(xié)同過濾算法研究的重點方向。目前的解決方法主要采用大型矩陣的降維技術(shù)，其中使用最為普遍和成功的就是基于SVD矩陣分解算法。但是，基于傳統(tǒng)SVD算法的特性，導(dǎo)致協(xié)同計算極為復(fù)雜，占用內(nèi)存大，運算時間長，這樣大大限制了它的實際應(yīng)用。因此，本文探討了一種新型的SVD算法RSVD，它將在語義分析系統(tǒng)中應(yīng)用效果較好的RI隨機索引技術(shù)，RRI兩次隨機索引算法與SVD結(jié)合，用RI對數(shù)據(jù)進(jìn)行預(yù)處理，對SVD奇異值分解進(jìn)行向量空間優(yōu)化。RSVD在movie lens電影數(shù)據(jù)集上的實驗結(jié)果表明，RSVD提高了推薦結(jié)果的精確度，減少了運算時間，同時提高了算法的可計算度。 關(guān)鍵字：推薦系統(tǒng)，協(xié)同過濾，降維，SVD（奇異值分解），RI（隨機索引），RRI（二次隨機索引），RSVD
Abstract：With the development of e-commerce, the commodity information of all kinds has been growing rapidly, but only a little part of it is useful to a certain user. Then how to pick up useful information efficiently and accurately for users has become a hot spot in e-commerce field and it is also the reason that recommender systems attract much attention. Collaborative filtering algorithm has been seen wildly applicated in recommender systems ,whose performance directly influences recommender systems. The main study of collaborative filtering focuses on how to reduce the negative effect brought by data sparsity . The proposed solution is to use matrix dimensionality reduction technique. However, SVD is limited practical by its high computing complexity, much heavy memory space and costs as well as running time. In this paper, we discuss a new SVD algorithm --- RSVD. In this algorithm, traditional SVD is combined with well-performed RI or RRI algorithm, in which RI or RRI is used for data preconditioning and SVD vector space optimization. The RSVD experiments on movie lens dataset indicate that RSVD improves the recommendation accuracy, reduces the running time and computing complexity. Keywords：Recommender system, Collaborative filtering, Dimensionality reduction, SVD ,Random Indexing, Reflective Random Indexing, RSVD
本論文中，預(yù)期類比實際應(yīng)用中處理數(shù)據(jù)的模式，采用一個稀疏矩陣，模擬仿真，代碼實現(xiàn)幾種算法，記錄實驗結(jié)果，并做數(shù)據(jù)分析，檢驗算法的性能。而關(guān)于RSVD算法的基礎(chǔ)思想是用RI技術(shù)對矩陣進(jìn)行初步的處理，也可以說是采樣，得到一個向量空間的基，最終提供全套的近似SVD矩陣。 應(yīng)用RSVD算法，期望其改進(jìn)算法的精確度，減少資源的消耗，同時，期望其能在提高推薦質(zhì)量的基礎(chǔ)上，使算法運算效率有所改進(jìn)。關(guān)于其實現(xiàn)過程，在后文中，有具體說明。
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