Nowadays people got used to short text answers, likes and shares. Anyone can feel it by entering popular forums or social networks. Modern platforms such as Twitter or Facebook contribute to this situation with their symbols per message limitations. The quality of content produced in such conditions is not high. According to Knight and Burn (2005): “The rapid growth of the Internet and the lack of enforceable standards regarding the information it contains has led to numerous information quality problems.” The possible solution to this problem is called mobile digital storytelling. It replaces traditional communication mechanisms (text, photos) with a digital narrative, thus making a stronger impact on user. Besides, it allows creating interesting content at any location with the help of mobile phone. However, it is hard to make a high quality story from scratch without prior experience. Viewing through previously created high quality content provides such experience. At the same time, reusing this content would allow creating story by combining and rearranging instead of producing from scratch. But state of the art mobile digital storytelling applications don’t provide any possibilities for content reuse. In addition, the influence of content reuse on the story creation process was not studied. Hence, in this work the researcher will explore and try to develop alternative ways to support content reuse in mobile digital storytelling (mDS). For this purpose a mechanism called RecSM (a recommendation system using content from Social Media) is introduced. The main goal of development is to create RecSM for mobile digital storytelling application. The influence of RecSM on mobile content reuse as well as the influence of reuse on storytelling process is the main study goals. The thesis is based on research conducted in Linnaeus University, Sweden. The research is divided into two main parts: gathering requirements for RecSM and the case study. 10 users are involved in both activities. Initial requirements for RecSM are defined after conducting research in the topic but final requirements are determined with the help of users. Based on them the RecSM is developed and added to a mobile digital storytelling application. A case study in Teleborg Castle (Vaxjo, Sweden) follows afterwards. Participants create stories about their castle experience with the help of mDS or mDS-RecSM application. The data for further research is retrieved through field notes, personal interviews and a survey. Then stories and answers of people that used mDS with and without recommender are compared and analyzed. Based on the study outcomes it is concluded that developed RecSM supports content reuse in mobile digital storytelling.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-12T14:22:55Z No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:15:27Z (GMT) No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Made available in DSpace on 2016-01-25T17:15:27Z (GMT). No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) Previous issue date: 2015-03-27 / Existem inúmeros recursos educacionais distribuídos em diferentes repositórios que abordam um conjunto amplo de assuntos e que possuem objetivos educacionais distintos. A escolha adequada desses recursos educacionais é um desafio para os usuários que desejam usá-los para a sua formação intelectual. Nesse contexto surgem os Sistemas de Recomendação para auxiliar os usuários nessa tarefa. Para que seja possível gerar recomendações personalizadas, torna-se importante identificar informações que ajudem a definir o perfil do usuário e auxiliem na identificação de suas necessidades e interesses. O uso constante e cada vez mais intenso de algumas ferramentas tecnológicas faz com que inúmeras informações a respeito do perfil, dos interesses, das preferências, da forma de interação e do comportamento do usuário possam ser identificadas em decorrência da interação espontânea que ocorre nesses sistemas. Esse é o caso, por exemplo, das redes socais. Neste trabalho é apresentada a proposta e o desenvolvimento de uma arquitetura capaz de extrair características do perfil e do contexto educacional dos usuários, através da rede social Facebook e realizar recomendações de recursos educacionais de forma individualizada e personalizada que sejam condizentes com essas características. A solução proposta é apoiada por técnicas de extração de informações e ontologias para a extração, definição e enriquecimento das características e interesses dos usuários. As técnicas de Extração de Informação foram aplicadas aos textos associados às páginas curtidas e compartilhadas por usuários nas suas redes sociais para extrair informação estruturada que possa ser usada no processo de recomendação de recursos educacionais. Já as ontologias foram usadas para buscar interesses relacionados aos temas extraídos. A recomendação é baseada em repositório de objetos de aprendizagem e em repositórios de dados ligados e é realizada dentro das redes sociais, aproveitando o tempo despendido pelos usuários nas mesmas. A avaliação da proposta foi feita a partir do desenvolvimento de um protótipo, três provas de conceito e um estudo de caso. Os resultados da avaliação mostraram a viabilidade e uma aceitação relevante por parte dos usuários no sentido de extrair informações sobre os seus interesses educacionais, geradas automaticamente da rede social Facebook, enriquecê-las, encontrar interesses implícitos e usar essas informações para recomendar recursos educacionais. Foi verificada também a possibilidade da recomendação de pessoas, permitindo a formação de uma rede de interesses em torno de um determinado tema, indicando aos usuários bons parceiros para estudo e pesquisa. / There are several educational resources distributed in different repositories that address to a wide range of subjects and have different educational goals. The proper choice of these educational resources is a challenge for users who want to use them for their intellectual development. In this context, recommendation systems may help users in this task.In order to be able to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her needs and interests. The constant and ever-increasing use of some technological tools allows the identification of different information about profile, interests, preferences, interaction style and user behavior from the spontaneous interaction that occurs in these systems, as, for example, the social networks. This paper presents the proposal and the development of one architecture able to extract users´ profile characteristics and educational context, from the Facebook social network and recommend educational resources in individualized and personalized manner, consistent with these characteristics. The proposed solution is supported by Information Extraction Techniques and ontologies for the extraction, enrichment and definition of user characteristics and interests. The Information Extraction techniques were applied to texts associated with “LIKE” and shared user´s pages on his social networks to extract structured information that can be used in the recommendation process of educational resources, the ontologies were used to search to interests related to extracted subjects. The recommendation process is based on learning objects repositories and linked data repositories and is carried out within social networks, taking advantage of user time spent at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation results show the viability and relevant users´ acceptance in order to extract information about their educational interests, automatically generated from the Facebook social network, enrich these information, find implicit interests and use this information to recommend educational resources. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.

The news landscape has changed during recent years because of the digitization. News can nowadays be found in both newspapers and on different sites online. The availability of the digital newspapers leads to competition among the news companies. To make the users stay on one specific platform for news, relevance is required in the content and oneway of creating relevance is through personalization, to tailor the content to each user. The focus of this thesis is therefore personalizing newspush notifications for a digital  newspaper and making them more relevant for users. The project was made in cooperation with VK Media, and their digital newspaper. The task in this thesis is to implement personalization of push notifications by building a recommendation system and to test the implemented system with data from VK. In order to perform the task, a dataset representing reading habits of VK’s users was extracted from their data warehouse. Then a user-based and content-based recommendation system was implemented in Python.The idea with the system is to recommend new articles that are sufficiently similar to one or more of the already read articles. Articles that may be liked by one of the most similar users should also be recommended. Finally, the system’s performance was evaluated with the data representing reading habits for VK’s users. The results show that the implemented system has better performance than the current solution without any personalization, when recommending a few articles to each user. The results from the evaluation also show that the more articles the users have read, the better predictions are possible to make. Thus, this thesis offers a first step towards meeting the expectations of more relevant content among VK’s users.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / This article is about the problem and development of a fashion virtual assistant proposed by using a Kinect v2 camera and image processing, for fashion retail stores. It comes up mainly as a response to the inability of providing unique experiences during the shopping process through the use of diverse devices. Because of this, similar virtual assistant solutions, oriented to provide clothing recommendations, were analyzed to be able to provide software that could give a more personalized suggestion for the users based on their physical characteristics. / Revisión por pares

<div><div><div><p>As technological capacity to store and exchange information progress, the amount of available data grows explosively, which can lead to information overload. The dif- ficulty of making decisions effectively increases when one has too much information about that issue. Recommendation systems are a subclass of information filtering systems that aim to predict a user’s opinion or preference of topic or item, thereby providing personalized recommendations to users by exploiting historic data. They are widely used in e-commerce such as Amazon.com, online movie streaming com- panies such as Netflix, and social media networks such as Facebook. Memory-based collaborative filtering (CF) is one of the recommendation system methods used to predict a user’s rating or preference by exploring historic ratings, but without in- corporating any content information about users or items. Many studies have been conducted on memory-based CFs to improve prediction accuracy, but none of them have achieved better prediction accuracy than state-of-the-art model-based CFs. Fur- thermore, A product or service is not judged only by its own characteristics but also by the characteristics of other products or services offered concurrently. It can also be judged by anchoring based on users’ memories. Rating or satisfaction is viewed as a function of the discrepancy or contrast between expected and obtained outcomes documented as contrast effects. Thus, a rating given to an item by a user is a compar- ative opinion based on the user’s past experiences. Therefore, the score of ratings can be affected by the sequence and time of ratings. However, in traditional CFs, pairwise similarities measured between items do not consider time factors such as the sequence of rating, which could introduce biases caused by contrast effects. In this research, we proposed a new approach that combines both structural and rating-based similarity measurement used in memory-based CFs. We found that memory-based CF using combined similarity measurement can achieve better prediction accuracy than model-based CFs in terms of lower MAE and reduce memory and time by using less neighbors than traditional memory-based CFs on MovieLens and Netflix datasets. We also proposed techniques to reduce the biases caused by those user comparing, anchoring and adjustment behaviors by introducing the time-aware similarity measurements used in memory-based CFs. At last, we introduced novel techniques to identify, quantify, and visualize user preference dynamics and how it could be used in generating dynamic recommendation lists that fits each user’s current preferences.</p></div></div></div>

The Internet increases the demand for the development of commercial applications and services that can provide better shopping experiences for customers globally. It is full of information and knowledge sources that might confuse customers. This requires customers to spend additional time and effort when they are trying to find relevant information about specific topics or objects. Recommendation systems are considered to be an important method that solves this issue. Incorporating recommendation systems in online social networks led to a specific kind of recommendation system called social recommendation systems which have become popular with the global explosion in social media and online networks and they apply many prediction algorithms such as data mining techniques to address the problem of information overload and to analyze a vast amount of data. We believe that offering a real-time social recommendation system that can understand the real context of a user’s conversation dynamically is essential to defining and recommending interesting objects at the ideal time. In this thesis, we propose an architecture for a real-time social recommendation system that aims to improve word usage and understanding in social media platforms, advance the performance and accuracy of recommendations, and propose a possible solution to the user cold-start problem. Moreover, we aim to find out if the user’s social context can be used as an input source to offer personalized and improved recommendations that will help users to find valuable items immediately, without interrupting their conversation flow. The suggested architecture works as a third-party social recommendation system that could be incorporated with other existing social networking sites (e.g. Facebook and Twitter). The novelty of our approach is the dynamic understanding of the user-generated content, achieved by detecting topics from the user’s extracted dialogue and then matching them with an appropriate task as a recommendation. Topic extraction is done through a modified Latent Dirichlet Allocation topic modeling method. We also develop a social chat app as a proof of concept to validate our proposed architecture. The results of our proposed architecture offer promising gains in enhancing the real-time social recommendations.

No description available.

Recommendation system benefits social media by attracting users with the posts they prefer. The recommended posts, however, may not align with what users really need to browse, especially in terms of emotion. Thus we conducted a case study in TikTok, in order to understand the emotional impact of social application’s post feed and to explore the interactive solution. The state-of-arts were reviewed, on the topics of psychology issues caused by social media, related therapy and product solutions. To empathise with users’ situation, a workshop was performed, consisting of a card game, presentation and participatory design. Then an emotion reminder, built on a Naive Bayesian text classifier and a facial expression SVM, was prototyped. With an accuracy of 0.51 (text) and 0.69 (facial expression) in sentiment classification, the emotion reminder was then tested by the users. It was discovered that users had higher emotion awareness, higher sense of control over the browsing and lower engagement in the interface with the prototype, compared with the original TikTok interface. And this was aligned with their needs described in the workshop. Users preferred the prototype’s content-based emotion detection than the detection based on their biological data in terms of privacy, and embraced the format of the reminder, instead of auto-filter, as an emotionally positive experience was not just browsing the posts with positive feelings, but receiving negative posts as well. / Rekommendationssystem gynnar sociala medier genom att locka användare med de inlägg de föredrar. De rekommenderade inläggen kan dock inte alltid överensstämma med det användarna verkligen behöver bläddra igenom, särskilt när det gäller känslor. Därför genomförde vi en fallstudie på TikTok för att förstå den emotionella påverkan av sociala applikationers inläggflöde och för att utforska interaktiva lösningar. Den senaste forskningen inom området granskades med fokus på psykologiska problem orsakade av sociala medier, relaterad terapi och produktlösningar. För att sätta oss in i användarnas situation genomfördes en workshop med ett kortspel, presentation och deltagande design. Därefter skapades en känslomässig påminnelse, baserad på en Naive Bayes-textklassificerare och en SVM för ansiktsuttryck. Med en noggrannhet på 0,51 (text) och 0,69 (ansiktsuttryck) i känslolägesklassificering testades sedan känslominnaren av användarna. Det visade sig att användarna hade ökad medvetenhet om sina känslor, ökad känsla av kontroll över bläddrandet och lägre engagemang i gränssnittet med prototypen jämfört med det ursprungliga TikTok-gränssnittet. Detta stämde överens med deras behov som beskrevs under workshopen. Användarna föredrog prototypens innehållsbaserade känslodetektion jämfört med detektering baserad på deras biologiska data av integritetsskäl och omfamnade formatet på påminnelsen istället för automatisk filtrering. En emotionellt positiv upplevelse handlade inte bara om att bläddra bland inlägg med positiva känslor, utan även att ta emot negativa inlägg.

Sättet människor konsumerar musik har förändrats genom historien och i samband med digitaliseringen har nya möjligheter att lyssna på musik uppstått. I samband med denna övergång har även fler algoritmbaserade tillvägagångssätt för musiklyssnande uppkommit. Syftet med studien är att undersöka unga vuxnas upplevelser av den algoritmbaserade Radiofunktionen på musikstreamingtjänsten Spotify. Studien har även undersökt huruvida de algoritmbaserade förslagen eventuellt påverkar användarnas upplevelser och musikbeteenden.Undersökningen är baserad på sju respondenters upplevelser inom åldersspannet 20-30 år, i denna studie definierat som unga vuxna. Studien har genomförts via kvalitativa metoder däribland en inledande dagboksstudie, med syfte att förbereda respondenternas reflektiva tänkande vilket följdes upp med semistrukturerade intervjuer. Därefter transkriberades intervjuerna för att senare kodas och en tematisk analys genomfördes. Resultatet av studien påvisar att det finns en variation i användandet av radiofunktionen samt att majoriteten av respondenterna uttryckt en positiv upplevelse av Radio på Spotify. Studien bidrar med nya insikter kring algoritmbaserade upplevelser hos användare i relation till musikstreaming och framförallt funktionen radio, samt hur användandet kan skilja sig beroende på situation och anledning till användande. / The streaming of music has during the last two decades become a new standard for how people acquire and listen to music. In correlation with this shift, other possibilities for listening to music have been on the rise. The purpose of this study is to investigate the experience for young adults of algorithm-based playlist ”Radio” on the streaming service platform Spotify. The investigation will determine whether or not these algorithm-based suggestions potentially affect the users' experience of listening to music and overall music behavior.The study is based on the experience of seven respondents of ages ranging between 20-30 years old - through this study referred to as "young adults." The qualitative methods this research has followed consists of a simple initial diary study, in order to prepare the respondents reflective thinking before the following semi-structured interviews. The interviews were then transcribed and followed by coding as well as a thematical analysis. The results of the study show that there is a variation in the use of the ”Radio” phenomenon on Spotify and that the vast majority of the respondents participating in the investigation expressed an overall positive experience. Furthermore, this study indicates that the respondents utilize the feature on different occasions and for different purposes.

在人們的日常生活中，推薦是很普遍的一種社會行為，它使人們不必親自去體驗所有的事物，可透過別人的經驗來得知一件事情或商品的好或壞。隨著科技的快速發展與網際網路的普及，電子商務已逐漸的融入社會，成為人類生活中不可或缺的一部分。然而在網路上過量的資訊，使得個人在資訊的使用與搜尋上面臨極大的挑戰，更加刺激了對於推薦資訊的需求，因此許多推薦技術相繼提出，推薦系統也應運而生，不僅使得推薦的範圍擴大了，推薦的型態也更為豐富多元；同時，在近年電子商務的發展中，對於個人化與顧客導向服務的愈益重視，使得推薦系統逐漸成為一種必要的線上服務。 在眾多的推薦技術之中，協同過濾推薦方法是最成功且最常被採用的推薦技術之一，許多台灣的拍賣平台上也都有採用類似概念的推薦系統，像是Yahoo!拍賣、露天拍賣上的評價機制均屬此類。然而，現行的拍賣評價機制都沒有採用社會網路的技術，本研究希望透過協同過濾與社會網路的結合，讓評價機制更趨於完備。 本研究以台灣最大的拍賣網站Yahoo!為例，蒐集了44萬筆交易記錄，並以推薦網(ReferralWeb)系統的矩陣方法為基礎，找出人與商品的關係、商品與類別的關係、人與人的關係，建立起一個社會網路，讓使用者可查詢特定領域的專家，並與之交易。除此之外，也可直接詢問專家關於商品的資訊或購買技巧。透過這樣的機制，希望能降低消費者在購買商品時所產生的交易糾紛，讓人們在網路上的購物體驗能變得更好。 / Nowadays, recommendation is a common social behavior between people. People can evaluate things or commodities from others’ experience and opinions instead of their own experiences. Along with the development of technology and Internet today, E-commerce has become an indispensable part of human life. However, due to the overloaded information, people face a fantastic challenge when accessing and searching on the Internet. Therefore, many methods of recommendation were proposed, and systems of recommendation are to come with the tide of fashion. In addition, the development of E-commerce emphasized on personalization and customer-oriented services more in recent years, which make recommendation system becomes a necessary on-line service gradually. Collaborative Filtering is the most successful and adopted one in numerous recommendation methods. There are many auction platforms in Taiwan also use recommendation systems, such like "Yahoo Auction", "Ruten Auction", etc. However, the previous mentioned recommendation mechanisms haven’t used Social Network technology; this study will propose an recommendation system which combines Collaborative Filtering and Social Network technology. This research collects 440,000 transaction data from the Yahoo auction platform, which is the biggest auction website in Taiwan. Based on the matrix method of ReferralWeb system(Shah, 1997), this research would like to build up the matrix of relationships between Person-Commodity, Commodity-Category, and Person-Person. Based on the three matrixes, finally builds up a Social Network. In the Social Network, users can enquire experts refer to the specific category of commodity, and then refer to the shops which the experts like or directly ask them the commodity information and purchase skill. Relying on the mechanism proposed by this research, our goals are to reduce the transaction disputes arising from consumers purchase commodities, and to let people have better experiences in on-line shopping.