隨著社會(huì)計(jì)算系統(tǒng)的蓬勃發(fā)展,越來(lái)越多的信息和特征被用于用戶建模,如畫(huà)像信息、位置、行為和偏好等。社交媒體為分析用戶情緒、個(gè)性等內(nèi)在狀態(tài)提供了各種各樣的資源。用戶的個(gè)性特征作為一種有價(jià)值的資源,可以反應(yīng)被研究用戶的內(nèi)在特點(diǎn),這啟發(fā)了一項(xiàng)新的研究領(lǐng)域,即個(gè)性計(jì)算�，F(xiàn)階段,該領(lǐng)域的研究大部分集中在通過(guò)分析用戶數(shù)據(jù)自動(dòng)識(shí)別用戶個(gè)性,很少將用戶個(gè)性特征納入到推薦系統(tǒng)中,更沒(méi)有研究用戶的個(gè)性特征對(duì)用戶建模、興趣挖掘過(guò)程以及推薦準(zhǔn)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(dòng)態(tài)建模的個(gè)性化感知用戶建模框架。為了證明該框架的高效性,我們?cè)O(shè)計(jì)了以下三個(gè)應(yīng)用場(chǎng)景:（1）提出了一種新穎的基于Big Five個(gè)性特征模型和混合過(guò)濾的朋友推薦系統(tǒng)。依靠個(gè)性特征和用戶和諧度實(shí)現(xiàn)推薦過(guò)程,實(shí)現(xiàn)了名為PersonNet的社交網(wǎng)站,以此證明該推薦系統(tǒng)的準(zhǔn)確率。（2）設(shè)計(jì)了一種基于動(dòng)態(tài)主題建模和Big Five個(gè)性特征的用戶興趣挖掘系統(tǒng)。為了驗(yàn)證在興趣挖掘過(guò)程中融入用戶個(gè)性特征的高效性,構(gòu)建了一個(gè)支持新聞共享的社交網(wǎng)絡(luò),并對(duì)收集到的數(shù)據(jù)進(jìn)行了不同的實(shí)驗(yàn)。（3）構(gòu)建了一種基于用戶興趣挖掘和元路徑發(fā)現(xiàn)的個(gè)... 

【文章頁(yè)數(shù)】：103 頁(yè)

【學(xué)位級(jí)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(jiǎn)歷及在學(xué)研究成果
學(xué)位論文數(shù)據(jù)集



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