本文選題：推薦系統(tǒng)　切入點(diǎn)：協(xié)同過濾　出處：《安徽工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)以及電子商務(wù)的迅速發(fā)展,網(wǎng)絡(luò)服務(wù)信息的種類和數(shù)量越來越多,由此造成了不可避免的信息過載現(xiàn)象。推薦系統(tǒng)在這種背景下應(yīng)運(yùn)而生。協(xié)同過濾算法是推薦系統(tǒng)的核心,它根據(jù)用戶的歷史活動行為和個人信息來挖掘其興趣偏好,幫助他們找到最感興趣的產(chǎn)品或服務(wù),為用戶提供高質(zhì)量的個性化推薦。然而在實(shí)際應(yīng)用中,該技術(shù)也面臨著一系列挑戰(zhàn)性問題。冷啟動和數(shù)據(jù)稀疏是目前協(xié)同過濾推薦技術(shù)中尚未得到有效解決的關(guān)鍵問題�，F(xiàn)存的基于協(xié)同過濾的用戶相似度算法在計算冷啟動用戶與其他用戶的相似度時,僅僅使用評分矩陣中的數(shù)值評分,忽略了用戶間共同評分的偏好差異、用戶自身的評分偏好以及項目流行度對用戶相似度的影響。這種情況下得到的用戶相似度的準(zhǔn)確性將大大地降低,因此很難準(zhǔn)確高效地預(yù)測出目標(biāo)用戶的興趣,最終導(dǎo)致協(xié)同過濾推薦算法產(chǎn)生的推薦結(jié)果準(zhǔn)確率不高。本文針對目前基于協(xié)同過濾的用戶相似度算法在處理新用戶冷啟動和數(shù)據(jù)稀疏問題時存在的一些問題進(jìn)行了詳細(xì)的分析研究,提出了一些改進(jìn)思路,并取得了一定的研究成果。主要內(nèi)容和創(chuàng)新點(diǎn)可歸納如下:1)提出一種考慮用戶間共同評分偏好差異的啟發(fā)式相似度算法。該算法基于PIP和MJD相似度算法的思想,利用用戶間共同評分的差值信息來計算用戶相似度。首先通過用戶間共同評分的差值比例計算出共同評分的各偏好權(quán)重;然后計算出每一種差值下的三種影響因子:Proximity、Impact和Popularity;最后,通過加權(quán)得到一個全局的用戶相似度。該算法同時考慮了評分?jǐn)?shù)據(jù)的特地領(lǐng)域含義和用戶間共同評分的偏好差異。有效地避免了不合理的用戶相似度增加,提高了相似用戶的區(qū)分度,共同評分偏好的權(quán)重計算相對簡單且不耗時。2)提出一種考慮流行度和用戶評分差異的啟發(fā)式相似度算法。該算法由三個相似度因子(PMSD、SD和Preference)構(gòu)成,考慮了在一個特定的數(shù)據(jù)集中項目流行度對用戶相似度的影響,并將其與均方差結(jié)合。算法充分利用了用戶評分信息包括數(shù)值信息和非數(shù)值信息,表達(dá)了用戶間不同的特征。另外,新算法還考慮了用戶間共同評分的偏好差異,并根據(jù)偏好差異給出不同的懲罰值。最后引入用戶評分的均值和方差反映了用戶的個人評分偏好。本文通過實(shí)驗測試了兩種新算法的性能,并與其他傳統(tǒng)的和改進(jìn)的用戶相似度算法進(jìn)行了比較。實(shí)驗對比結(jié)果和理論分析表明,在新用戶冷啟動和數(shù)據(jù)稀疏條件下,本文所提算法在MAE、覆蓋度、準(zhǔn)確度以及召回率上都取得了比較優(yōu)越的表現(xiàn),顯著地提高了協(xié)同過濾算法的預(yù)測精度和推薦系統(tǒng)的推薦質(zhì)量。
[Abstract]:With the rapid development of Internet technology and electronic commerce, the variety and quantity of network service information are more and more. Under this background, collaborative filtering algorithm is the core of recommendation system, which is based on the user's historical activity behavior and personal information. Help them find the product or service they are most interested in and provide users with high-quality personalized recommendations. However, in practical applications, This technology also faces a series of challenging problems. Cold start and data sparsity are the key problems that have not been effectively solved in collaborative filtering recommendation technology. The existing user similarity algorithm based on collaborative filtering is computing. When a cold boot user is similar to other users, Using only the numerical scores in the scoring matrix ignores the differences in preferences between users for common ratings. In this case, the accuracy of the user similarity will be greatly reduced, so it is difficult to accurately and efficiently predict the interest of the target user. Finally, the recommendation accuracy of collaborative filtering recommendation algorithm is not high. This paper aims at some problems existing in the current user similarity algorithm based on collaborative filtering in dealing with the problems of cold start and data sparsity of new users. Have carried out detailed analysis and research, Some improvements are put forward. The main contents and innovations can be summarized as follows: 1) A heuristic similarity algorithm considering the differences of users' common scoring preferences is proposed. The algorithm is based on the idea of PIP and MJD similarity algorithms. The user similarity is calculated by using the difference information of the common score between users. First, the weight of each preference of the common score is calculated by the difference ratio of the common score among users; then the three influence factors under each difference are calculated:: maximum impact and popularity. finally, A global user similarity is obtained by weighted method. The algorithm takes into account the special domain meaning of the scoring data and the preference difference of the users' common rating, which effectively avoids the unreasonable increase of user similarity. A heuristic similarity algorithm considering the difference between popularity and user rating is proposed. The algorithm is composed of three similarity factors, PMSDSD and preference. The influence of item popularity on user similarity in a given dataset is considered and combined with RMS. The algorithm makes full use of user scoring information, including numerical information and non-numerical information. In addition, the new algorithm also takes into account the differences in preferences of users' common scores. Finally, the mean and variance of the user's score reflect the user's personal rating preference. The performance of the two new algorithms is tested by experiments in this paper. Compared with other traditional and improved user similarity algorithms, the experimental results and theoretical analysis show that under the new user cold start and data sparse conditions, the proposed algorithm in mae, coverage, The accuracy and recall rate are superior to each other and the prediction accuracy of collaborative filtering algorithm and the recommendation quality of recommendation system are improved significantly.
【學(xué)位授予單位】：安徽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3;F274

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