隨著網際網路的興起與快速成長，越來越多的資訊在此新傳媒中散播與分享。但是大量的資訊反而對使用者造成另一種困擾，因為使用者的資訊處理能力有限，而造成資訊過載問題。因此推薦系統隨之興起，利用使用者過去的喜好或是其他資訊來源的協同喜好，幫助使用者獲取所需的資訊。
不過過去相關的文獻研究大多著重於個人化推薦，但是隨著社群網絡探討的興起，群組推薦變成一個重要的研究議題。群組推薦比個人化推薦複雜，因為它是對有意或隨機地參與社群活動的一群人進行推薦，而這樣的推薦要考量群組整體的喜好行為，而非僅止於每一個人的喜好。
因此本研究的目的在於提出混合式過濾方法進行群組文件推薦。其中，我們應用了潛在狄利克里分配(latent Dirichlet allocation, LDA)模式找出文件潛在主題分佈，其結果可以提供內容式過濾方法與協同過濾方法的連結，達到互補與加成的績效。我們接著進行二組共四個實驗來驗證所提方法，實驗結果顯示我們所提方法(GCBPF and GSBCF)有不錯的績效表現，亦優於傳統群組推薦過濾方法。這樣的結果因此驗證了我們所提方法的應用可行性。

With the emergence of Internet, there is more and more information disseminating all over this channel. The abundant amount of information, however, causes difficulty for users to locate desired information, which is referred to as the information overload problem due to our limited processing ability. Therefore, recommender systems arise to assist users to acquire useful information based on their past preferences or collaborative preferences from other sources.
Most of the previous research works focus on personalized recommendation for individuals. However, a more difficult issue is to make group recommendation. Group recommendation aims at recommending items for a group of users who participate in a social activity intentionally or randomly. It notably distinguishes itself from personalized recommendation in that collective group behavior needs to be addressed by taking individuals’ behaviors into account.
The objective of this research is thus to propose hybrid filtering approaches for group recommendation on documents. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to serve as a bridge to connect content-based filtering and collaborative filtering as a whole, and generate complementary and additive effects for better performance. Four experiments (two random group cases and two intentional group cases) are conducted accordingly. The results show that our proposed approaches (GCBPF and GSBCF) outperform other traditional group filtering approaches on the recommendation performance, which justifies the feasibility of our proposed approaches in applications.

Contents
論文審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
致謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objective of the research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organization of the research . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Recommender systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Content-based filtering methods . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 Collaborative filtering methods . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 The hybrid methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Document recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Group recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Latent topic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 Probabilistic latent semantic analysis . . . . . . . . . . . . . . . . . . 16
2.4.2 Latent Dirichlet allocation . . . . . . . . . . . . . . . . . . . . . . . . 18
Chapter 3 Proposed Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 Group Rating Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 LDA Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Group Collaborative-Based Profile Filtering . . . . . . . . . . . . . . . . . . . 26
3.4 Group Semantic-Based Collaborative Filtering . . . . . . . . . . . . . . . . . . 28
3.5 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Chapter 4 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1.2 Performance Measure . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.1.3 Group Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1.4 Evaluation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Experiment I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3 Experiment II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4 Experiment III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5 Experiment IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Chapter 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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