發(fā)布時間：2022-12-10 00:44

　　隨著社會計算系統(tǒng)的蓬勃發(fā)展,越來越多的信息和特征被用于用戶建模,如畫像信息、位置、行為和偏好等。社交媒體為分析用戶情緒、個性等內在狀態(tài)提供了各種各樣的資源。用戶的個性特征作為一種有價值的資源,可以反應被研究用戶的內在特點,這啟發(fā)了一項新的研究領域,即個性計算�，F(xiàn)階段,該領域的研究大部分集中在通過分析用戶數據自動識別用戶個性,很少將用戶個性特征納入到推薦系統(tǒng)中,更沒有研究用戶的個性特征對用戶建模、興趣挖掘過程以及推薦準確性的影響。本文提出了一種基于Big Five人格模型和用戶興趣動態(tài)建模的個性化感知用戶建�？蚣�。為了證明該框架的高效性,我們設計了以下三個應用場景:（1）提出了一種新穎的基于Big Five個性特征模型和混合過濾的朋友推薦系統(tǒng)。依靠個性特征和用戶和諧度實現(xiàn)推薦過程,實現(xiàn)了名為PersonNet的社交網站,以此證明該推薦系統(tǒng)的準確率。（2）設計了一種基于動態(tài)主題建模和Big Five個性特征的用戶興趣挖掘系統(tǒng)。為了驗證在興趣挖掘過程中融入用戶個性特征的高效性,構建了一個支持新聞共享的社交網絡,并對收集到的數據進行了不同的實驗。（3）構建了一種基于用戶興趣挖掘和元路徑發(fā)現(xiàn)的個... 

【文章頁數】：103 頁

【學位級別】：博士

【文章目錄】：
Acknowledgements
摘要
Abstract
Chapter Ⅰ Introduction
    1.1 Social computing
    1.2 Personality traits theory
    1.3 Personality computing
    1.4 Recommendation systems
    1.5 User interest mining
    1.6 Problem statement and research questions
    1.7 Innovations and contributions
        1.7.1 Personality-aware friend recommendation system
        1.7.2 Personality-aware user interest mining system
        1.7.3 Personality-aware product recommendation system
    1.8 Thesis structure
Chapter Ⅱ Related works
    2.1 Automatic personality recognition
        2.1.1 Text-based APR
        2.1.2 Image-based APR
        2.1.3 Gaming and Behavior-based APR
    2.2 Personality enabled social robots
    2.3 Personality in recommendation systems
        2.3.1 Friend recommendations
        2.3.2 Multimedia recommendations
        2.3.3 Academic content recommendations
        2.3.4 Product recommendations
    2.4 User interest mining
Chapter Ⅲ PersoNet: Friend Recommendation System Based on Big FivePersonality Traits and Hybrid Filtering
    3.1 Introduction
    3.2 Notations
    3.3 System model
    3.4 Similarity measurement
    3.5 Recommendation system
    3.6 Experiment details
        3.6.1 Data
        3.6.2 Participants
        3.6.3 Personality measurement
        3.6.4 Data collection phase
        3.6.5 Harmony rating
        3.6.6 Friend recommendations
        3.6.7 Testing phase
    3.7 Performance evaluation
        3.7.1 Implementation
        3.7.2 Evaluation metrics
        3.7.3 Results discussion
    3.8 Conclusions
Chapter Ⅳ: Mining User Interest Based on Personality-aware Hybrid Filtering inSocial Networks
    4.1 Introduction
    4.2 Notations
    4.3 Representation model
        4.3.1 User modeling
        4.3.2 Topic modeling
        4.3.3 Implicit interest prediction
    4.4 System evaluation
        4.4.1 Dataset and experiment details
        4.4.2 Variants
        4.4.3 Baselines
        4.4.4 Evaluation metrics
        4.4.5 Results analysis and discussion
    4.5 Conclusions
Chapter Ⅴ: Personality-aware Product Recommendation System based on InterestMining and Meta-path Discovery
    5.1 Introduction
    5.2 Notations
    5.3 System design
    5.4 Representational model
        5.4.1 Users representation
        5.4.2 Topics representation
        5.4.3 Items representation
    5.5 Interest mining
    5.6 Item mapping
    5.7 Meta path discovery
    5.8 Evaluation
        5.8.1 Baselines
        5.8.2 Evaluation metrics
        5.8.3 Dataset description
    5.9 Results discussion
    5.10 Conclusions
Chapter Ⅵ: Conclusion and future directions
References
作者簡歷及在學研究成果
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