Since the emergence of Recommender Systems (RS), most of the research has focused on improving the capability of a recommender system to predict and provide an accurate recommendation. However, the literature has demonstrated increasing evidence that providing accurate recommendations is not sufficient to increase users’ acceptance of the provided recommendations. Hence, it is vital for a recommender system to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. Consequently, there becomes a need for new research paradigms to help improve the capabilities of recommender systems, which goes beyond the recommendation accuracy. One of the recently emerged research directions that consider this need fosters the idea of adopting human-related theories from the social sciences domain, such as persuasiveness of social communication. In this context, however, a challenging, non-trivial, and not fully explored issue that arises is: how to integrate human-related theories into a recommender system to be one of its intrinsic characteristics in order to improve its performance beyond its accuracy? This thesis aims to address the above issue from two angles: first, it investigates improving recommender systems by increasing users’ acceptance of the recommendations. To achieve this, the influence of persuasion principles on users of recommender systems is investigated. Then a reference architecture framework to adapt and integrate persuasion features as a substantial characteristic of recommender systems is proposed. The proposed framework, named Personalized Persuasive RS (PerPer), adopts concepts from the social sciences literature, namely personality traits and persuasion principles. In addition, PerPer adapts machine learning concepts, in particular, the Learning Automata, to support its learning capabilities. Second, the thesis discusses evaluating recommender systems beyond their accuracy. Particularly, it proposes two evaluation approaches that aim to evaluate recommender systems in a comprehensive way that goes beyond evaluating accuracy only. The first evaluation approach is called the Comprehensive Performance evaluation (ComPer). It adopts concepts from the human learning domain and provides a simple, thorough, and setting-independent evaluation approach for recommenders. The essence of ComPer is to consider a recommender system as a human being, and hence the former’s outcomes (i.e., recommendations) can be evaluated and validated in a way similar to how humans’ learning outcomes are evaluated. The second evaluation approach adopts goal-oriented modeling to provide an evaluation that does not only assess recommenders beyond their accuracy but also considers the multi-stakeholders of RSs. We demonstrate, empirically, and by user studies, the feasibility and usefulness of the proposed approaches. The contributions of the thesis are: (1) A characterization of recommender systems as systems supported with human traits and features, which goes beyond the conventional recommender systems known in the literature. (2) A user study that examines the impact of persuasive principles on users of recommender systems. (3) A Personalized Persuasive RS (PerPer) reference architecture framework to enrich recommender systems with persuasion capabilities that are personalized and adaptive for different users. (4) A mapping between human’s cognitive skills and the recommendation process. (5) The Comprehensive Performance evaluation (ComPer) framework to provide a comprehensive assessment of recommender systems considering multiple evaluation dimensions other than accuracy. And (6) a goal-oriented evaluation approach to assess the impact of multiple alternatives for recommendation approaches on the satisfaction of RSs stakeholders’ goals.

運動依從性在健康管理議題裡是非常重要的一環，現在的人經常感到身體不適而診斷結果卻正常，這種現象根據世界衛生組織的定義為「亞健康」或「健康的灰色地帶」。在高壓的工作環境或不正常的生活作息下，最容易有這些徵狀，儘管大家都知道長期規律的運動可以促進健康，實際實行的狀況卻不如預期的好。為了改善此狀況，本研究透過蘋果公司的套件(ResearchKit)開發行動應用程式來幫助使用者增加運動依從性。此套件內建許多模組供開發者與研究人員使用，如聲明宣告與問卷模組等，讓整個研究、開發流程更為快速有效。本研究流程遵行設計科學方法論來創造一個設計實體，即為本研究開發的應用程式「Active Track」。 在設計階段，本研究採用了「說服設計準則」中的「Tailoring」與「Reminder」設計方法，意圖強化、形塑甚或改變使用者對於目標行為的態度，透過本應用程式來激勵使用者改變自身行為，達到目標設定。目前市面上許多健康管理的應用程式皆已證實個性與說服科技之間的相關性，因此本研究採用了MBTI適性分析工具並設計出相對應的激勵文字訊息，期望透過此設計實體來協助使用者增加運動依從性。 在第一階段的實驗評估，我們於166個下載人次當中篩選出87個有效樣本來比較樣本之間的表現，其中有54人有接收激勵文字訊息，33人則無。平均而言，那些有收到激勵文字訊息的受測者，完成率較沒有收到激勵文字訊息的受試者高出百分之十五。然而在訊息類型與個性是否相符的比較實驗當中，訊息符合與不符合使用者個性的結果之間並無顯著差異。本研究之結果僅顯示出透過激勵文字訊息可以有效督促使用者完成運動目標。在第二階段的實驗中加入了「訊息重複性」、「回饋機制」因子來改良應用程式，研究結果顯示訊息的重複整體而言可以提升百分之二十三的完成率，其中適性結果為「理性」的受測者則有百分之二十七的提升，然而回饋機制設計在本研究並無統計顯著。 在兩階段的設計循環下，本研究證實透過Active Track重複地傳送激勵文字訊息可以協助使用者增加運動依從性，進而降低罹患疾病的風險，研究結果對於未來說服系統之開發以及其他醫療領域提升依從性之相關研究也提供了良好的參考價值。 / Adherence to an exercise schedule is valuable for health management. Nowadays, most people have experienced uncomfortable feelings but diagnostic data are normal. The phenomenon is called ‘sub-health’ condition, which is a state between health and disease. People are likely to experience discomfort if their working environment is stressful and their lifestyle is unhealthy. Therefore, a long period of commitment to adhere to physical activity programs is beneficial for people’s health. Although people would benefit from support to increase exercise compliance, adherence to physical activity plans is often very low. To address these shortcomings, this paper introduces a low-cost method–an iOS application developed using Apple Inc.’s ResearchKit–to help people adhere to their physical activity plans. ResearchKit provides various modules such as consent declaration and survey task for helping researchers create a research app more efficiently. We applied design science methodology to create a design artifact, namely Active Track. By including the “Tailoring” and the “Reminder” persuasive principles in Active Track to develop, strengthen, or change attitudes or behaviors, the design artifact can act as support instruments that stimulate and encourage users to comply with target behavior. Because studies of health-promotion apps have identified the correlations between personality and persuasive technology, we used the Myer–Briggs Type Indicator personality assessment to design motivational messages for each type of personality as a text reminder in Active Track. In the first evaluation stage of Active Track, we identified 87 valid participants (54 with motivational messages and 33 without motivational messages) from 166 downloads for performance comparison. On average, the completion rates of participants who were presented with motivational messages were 15% higher, but the difference in message matching experiment was not significant. The results demonstrated that our design approach is able to improve adherence on exercise plans by providing users with motivated messages. Therefore, we implemented repetitiveness and feedback intervention in a further design iteration and evaluated the improvement in Active Track by using these two new factors. The results showed that the repetitiveness factor enhanced the completion rate by approximately 23%; in particular, participants who were identified as having Thinking-type MBTI personalities exhibited an improvement of approximately 27% due to repetitive messages. However, feedback information had no significant effect on adherence. In summary, the findings of this study confirmed that Active Track can help individuals to improve their exercise adherence through repetitive motivational messages, reduce the risk of diseases, and provide useful insights for the future development of persuasive systems and studies into adherence enhancement for health care.