臺灣博碩士論文加值系統

在推薦系統中協同過濾 (Collaborative filtering) 是目前最廣泛使用的方法之一，而此方法最重要的組成部分便是透過使用者項目矩陣 (User-item matrix) 找到相似的使用者或是項目，並以此來進行產品的推薦。然而傳統協同過濾的方法在計算目標使用者與其他使用者的相似度時，並沒有考慮到目標項目與其他共同評分項目 (Co-rated items) 之間的關係，更精確地說它們給予共同評分過的項目相同的權重。但是我們認為在計算使用者之間的相似度時，目標項目與其他共同評分項目之間的關係，是一個非常重要的因素。鑑此，我們提出一個新的相似度計算方式，此方法不僅考慮了目標項目與其他共同評分項目之間的關聯性，以及共同評分的比例並且針對冷開機問題 (Cold-start problem) 結合了人工生成資訊 (Profile expansion)的方法。而從實驗的結果也顯示出我們所提出的方法不只對於一般情況下有很好的推薦效果，對於冷開機狀況下仍然有不錯的推薦準確度。

Collaborative filtering is one of the most widely used methods to provide product recommendation in online stores. The key component of the method is to find similar users or items by using user-item matrix so that products can be recommended based on the similarities. However, traditional collaborative filtering approaches compute the similarity between a target user and the other user without considering a target item. More specifically, they give an equal weight to each of the items which are rated by both users. However, we think that the similarity between the target item and each of the co-rated items is a very important factor when we calculate the similarity between two users. Therefore, in this paper we propose a new similarity function that takes similarities between a target item and each of the co-rated items and the proportion of common ratings into account. In addition, we also combine the item genre to the profile expansion to reinforce our model in order to deal with the cold-start problem. Experimental results from MovieLens dataset show that the method improves accuracy of recommender system significantly.

指導教授推薦書 II
論文口試委員審定書 III
Abstract 　　　　IV
論文摘要 　　　　　　　　　V
致 謝 　　　　　　　　　VI
Table of Contents VII
List of Figures VIII
List of Tables IX
1. Introduction 1
1.1 Background 1
1.2 Motivation and Contribution 2
1.3 Thesis Organization 3
2. Related Works 4
2.1 Content Based Filtering 4
2.2 Collaborative Filtering 4
2.3 Hybrid Recommender System 5
3. Proposed Method 7
3.1 System Architecture 8
3.2 Weighted Distance Model 9
3.3 Profile Expansion 11
3.4 Predicting Ratings on Target Items 13
4 Experiment Study 14
4.1 Datasets 14
4.2 Experimental Setup 14
4.3 Experimental Results 16
4.3.1 MAE Analysis 16
4.3.2 Comparisons of Recall and Precision 18
4.3.3 Comparisons of Execution Time 19
5 Conclusion and Future Works 21
Reference 23

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