【摘要】：隨著互聯(lián)網(wǎng)的快速發(fā)展，Web成為了人們獲取信息的主要途徑。然而由于電子商務(wù)的廣泛普及，如何為用戶提供有用的信息成為了一個研究熱點。雖然搜索引擎的出現(xiàn)在一定程度上滿足了人們對信息檢索的需求，但是無法滿足不同領(lǐng)域、不同層次的用戶需求。因此，個性化推薦技術(shù)作為個性化服務(wù)的一種模式應(yīng)信息檢索的需求而產(chǎn)生，其本質(zhì)是信息過濾。 推薦系統(tǒng)作為解決信息過載的重要工具，，為用戶提供如電影、音樂、書籍及新聞等方面的個性化推薦。在過去的十年里，研究者致力于各種推薦技術(shù)的探究并將其應(yīng)用到實際系統(tǒng)中。協(xié)同過濾推薦是目前最為經(jīng)典并為廣泛應(yīng)用的推薦技術(shù)，它根據(jù)目標(biāo)用戶的偏好以及與該用戶具有相似偏好的用戶的項目評價，向目標(biāo)用戶進(jìn)行新項目的推薦或評分預(yù)測。然而，協(xié)同過濾技術(shù)存在冷啟動和數(shù)據(jù)稀疏等問題。 基于聯(lián)合相似度的協(xié)同過濾算法，將社會網(wǎng)絡(luò)分析的方法引入到協(xié)同過濾推薦系統(tǒng)中。利用用戶-項目二部圖、用戶-用戶單部圖以及基于相同瀏覽行為模式的行為網(wǎng)絡(luò)圖分別生成相似度矩陣，然后依據(jù)相似度矩陣的密度來確定其在聯(lián)合相似度中的權(quán)重，最終生成聯(lián)合相似度。最后在豆瓣數(shù)據(jù)集上將此算法與現(xiàn)存的一些評分預(yù)測及推薦算法進(jìn)行了對比試驗，試驗結(jié)果表明基于聯(lián)合相似度的協(xié)同過濾算法在評分預(yù)測及推薦結(jié)果上更加精確。
[Abstract]:With the rapid development of the Internet, Web has become the main way for people to obtain information. However, due to the widespread popularity of electronic commerce, how to provide useful information for users has become a research hotspot. Although the emergence of search engines to some extent meet the needs of information retrieval, but can not meet the different fields, different levels of user needs. Therefore, personalized recommendation technology, as a mode of personalized service, comes into being according to the requirement of information retrieval, and its essence is information filtering. As an important tool to solve information overload, recommendation system provides personalized recommendation for users such as movies, music, books and news. Over the past decade, researchers have devoted themselves to the exploration of various recommended technologies and their application to practical systems. Collaborative filtering recommendation is the most classical and widely used recommendation technology at present. According to the preference of the target user and the item evaluation of the user with similar preference, the collaborative filtering recommendation can recommend or predict the new item to the target user. However, there are some problems in collaborative filtering technology, such as cold start and data sparsity. Based on the collaborative filtering algorithm of joint similarity, the social network analysis method is introduced into collaborative filtering recommendation system. The similarity matrix is generated by the user-item bipartite graph, the user-user single-part graph and the behavior network graph based on the same browsing behavior pattern, and their weights in the joint similarity are determined according to the density of the similarity matrix Finally, the joint similarity is generated. Finally, the algorithm is compared with some existing score prediction and recommendation algorithms on the soybean valve dataset. The experimental results show that the joint similarity based collaborative filtering algorithm is more accurate in score prediction and recommendation results.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3

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