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標題: | 於圖形基礎之協同過濾架構上融和信任關係與相似度之Top-k項目推薦 Fusing Trust and Similarity on Graph-based Collaborative Filtering Framework for Top-k Item Recommendation |
作者: | Yen-Kai Huang 黃焱鍇 |
指導教授: | 廖婉君(Wanjiun Liao) |
關鍵字: | 推薦系統,協同過濾,社群網路,信任網路,網路科學, Recommender System,Collaborative Filtering,Social Network,Trust Network,Network Science, |
出版年 : | 2013 |
學位: | 碩士 |
摘要: | 協同過濾是目前推薦系統中最廣為運用的方法之一。針對在推薦系統中更為符合產業需求的前k項之項目推薦為目標,利用信任關係建立社群親近度、並融合了使用者相似度來加強推薦系統準確度。此研究提出一個基於圖形基礎的協同過濾架構。基於考量使用者在資料中的變異性個人化,針對每一個使用者在資料中的表現建立自我網路,並且分為社群相近度層、相似使用者層以及相似項目層,我們進而發展預測各層的信心指數的機制:利用網路科學的理論基礎─群聚係數,分析各層內物件所組成的圖的相似性,以判斷使用者在系統上行為的一致性,藉而估計出對於此層推薦路徑的可信度。最後以可信度融合三層的推薦路徑以預測該使用者之後可能評分的項目對象,產生出前k項之推薦結果。我們利用來自真實世界社群網路服務中的資料,研究各參數以及相似度計算的設定在不同服務的資料庫中的表現及對推薦結果準確度的影響。實驗顯示,基於此圖形架構下的融合機制可以有效地彌補不同方法之間的缺漏,增加推薦名單的命中準確性,並可從推薦路徑中提供使用者對於推薦結果的解釋,以增進推薦系統中的使用者經驗。 Collaborative filtering is one of the most widely used memory-based approaches in recommender systems. CF uses the known preferences of a group of users to recommend or predict the unknown preferences for other users. Top-n item recommendation is an important tasks for recommendation and more realistic than value prediction for e-commerce system design. To improve the accuracy performance of recommendation, in the thesis we first introduce the trust into the system and exploit proximity to enhance CF. Second, we propose a general graph probabilistic model fusing item-based, user-based and proximity-based collaborative filtering by path ensemble from three layers. The model is consequently more robust to data sparsity. Further, to deliberate the confidence from these three sources, based on the framework we propose the CCR estimation mechanism to weigh the importance between each predictor of layer graphs. The mechanism exploits the classic network property, clustering coefficient, on ego network of target user by the simple intuition of consistency of user behavior. To evaluate the different predictors, the hit ratio metric is proposed to measure the accuracy quality of fusion of predictors. Experiments on real datasets demonstrate that the proposed methods are indeed more effective against accuracy. The results show the fusion by CCR estimation mechanism can improve the recall performance and hit ratio of fusion on the both datasets. Thus, the accuracy and diversity requirement could be achieved in the same time. The experiments also show that the fusion predictor contains proximity based CF is helpful especially on the cold users. Besides, the proposed graph-based framework is able to provide the explanation by the recommendation path, the user experience of the system could be promoted. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60429 |
全文授權: | 有償授權 |
顯示於系所單位: | 電機工程學系 |
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