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研究生:黃宇陽
研究生(外文):Yu-Yang Huang
論文名稱:以跨網站使用者興趣傳遞輔助冷啟動推薦系統
論文名稱(外文):Improving Cold-Start Recommendation with a Cross-Site User Interest Transfer Model
指導教授:林守德林守德引用關係
指導教授(外文):Shou-De Lin
口試委員:陳信希鄭卜壬蔡宗翰駱宏毅
口試日期:2015-07-21
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:42
中文關鍵詞:冷啟動推薦協同過濾轉移學習最近鄰居法矩陣分解
外文關鍵詞:Cold-start problemCollaborative filteringTransfer learningNeighborhood methodsMatrix factorization
相關次數:
  • 被引用被引用:1
  • 點閱點閱:367
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本論文嘗試使用跨網站使用者興趣傳遞解決推薦系統中的冷啟動問題。一般來說,推薦系統使用評分和文字兩種資訊將感興趣的物品推薦給使用者。然而使用這兩種資訊時皆可能遇到所謂的「冷啟動問題」,也就是當使用者過往對物品的評分記錄不多,或甚至無任何記錄時,推薦系統將無法對使用者進行有效的推薦。常用於解決冷啟動問題的方法是引入輔助資料。現今許多使用者都同時活躍於多個社群網站,同時這些網站之間彼此也多有連接帳戶的機制。假設能夠從相互連接的其他網站中獲取資料,就能夠利用這些輔助資料解決新進用戶的冷啟動問題。然而上述的「跨網站興趣傳遞」策略中隱含著一個重大的難點,即不同網站之間往往並不具有相同的物品評分機制,或相同的文字資料結構。本論文的優勢在於能利用不具特定結構的一般文字,將一網站的資訊帶到另一網站的推薦系統之中。確切來說,本論文使用主題模型從一網站抽取關於使用者的特徵向量,以計算使用者之間的相似度。並修改機率型矩陣分解模型,利用相似度計算最近鄰居,將最近鄰居的隱含向量收集起來,形成一組「最近鄰居虛擬樣本」。利用這組加有權重的虛擬樣本,便得以估計冷啟動用戶隱含向量的機率分配,進而對此用戶形成推薦清單。在實驗部分使用一組現實生活中的跨網站資料集驗證此方法的有效性,和過往提出的模型相比,本論文提出的方法尤其在冷啟動問題嚴重的狀況下,取得相當程度的優勢。

In this work, we attempt to transfer user interests across websites for cold-start recommendation. Both rating-based and text-based recommender systems may suffer from the cold-start problem. One effective way to ease the cold-start problem is to introduce auxiliary data. Users nowadays hold multiple accounts across websites. If data can be obtained via the account linking mechanism, there will be an abundant supply of auxiliary data. Although this cross-site approach can be exploited to solve the cold-start problem, it is often the case that we have to deal with heterogeneous data when transferring knowledge across websites. In this work, we make use of unstructured auxiliary text to solve the cold-start problem. In particular, we extract topic vectors from source-domain text, and use the similarity scores between users to construct "nearest-neighbor pseudo data", a set of weighted (pseudo) samples which can be used to estimate the unknown parameters of the distribution over the user latent factors in the target domain. The inference process and model structure of the probabilistic matrix factorization has been modified to utilize this pseudo dataset. Improvement over previous methods, especially for the cold-start users, has been demonstrated with experiments on a real-world cross-website dataset.

摘要 iii
Abstract iv
1 Introduction 1
1.1 Cold-Start Problem in Recommender Systems 1
1.2 Cross-Site User Interests Transfer 2
1.3 Nearest-Neighbor Pseudo Data 3
1.4 Contributions 4
2 Related Work 6
2.1 Matrix Factorization (MF) 6
2.2 Probabilistic Matrix Factorization (PMF) 7
2.3 Collaborative Topic Regression (CTR) 9
2.4 Nearest-Neighbor Method 10
3 Methodology 12
3.1 CTR-Based Transfer Model 12
3.2 Nearest Neighbor Pseudo Data (NNPD) Framework 13
3.3 Simple Example: Unknown Mean 15
3.4 More Generalized Models 20
4 Experiment 24
4.1 Dataset and Statistics 24
4.2 Evaluation and Scenario 26
4.3 Baseline Methods 26
4.4 Proposed Hypotheses 27
4.5 Pairwise User Similarity Matrices 27
4.6 In-Matrix Prediction 29
4.7 Out-of-Matrix Prediction 35
5 Conclusion 38
Bibliography 40

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