An explainable recommender system based on semantically-aware matrix factorization.

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without a compromise in trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. Semantic Web (SW) technologies have been exploited increasingly in recommender systems in recent years. The SW consists of knowledge graphs (KGs) providing valuable information that can help improve the performance of recommender systems. Yet KGs, have not been used to explain recommendations in black box systems. In this dissertation, we exploit the power of the SW to build new explainable recommender systems. We use the SW\u27s rich expressive power of linked data, along with structured information search and understanding tools to explain predictions. More specifically, we take advantage of semantic data to learn a semantically aware latent space of users and items in the matrix factorization model-learning process to build richer, explainable recommendation models. Our off-line and on-line evaluation experiments show that our approach achieves accurate prediction with the additional ability to explain recommendations, in comparison to baseline approaches. By fostering explainability, we hope that our work contributes to more transparent, ethical machine learning without sacrificing accuracy

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automatsko povećanje pamtljivosti slika

The dissertation considers the problem of automatic increase of image memorability. The problem-solving approach is based on editing-byapplying-filters paradigm. Given an arbitrary input image, the proposed deep learning model is able to automatically retrieve a set of “style seeds”, i.e., a set of style images which, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the approach with experiments, performing both a quantitative evaluation and a user study.Дисертација разматра проблем аутоматског повећања памтљивости фотографије на основу модела дубоког учења. Овој проблематици се приступа са аспекта развоја иновативног приступа заснованог на парадигми уређивања слике применом филтера. Арбитрарна улазна слика аутоматски преузима сет стилских карактеристика који се преносе путем алгоритма неуронског стила, омогућавајући на овај начин пораст памтљивости целокупне слике. Ефикасност предложеног приступа евалуирана је експерименталнo уз изведбу корисничке студије.Disertacija razmatra problem automatskog povećanja pamtljivosti fotografije na osnovu modela dubokog učenja. Ovoj problematici se pristupa sa aspekta razvoja inovativnog pristupa zasnovanog na paradigmi uređivanja slike primenom filtera. Arbitrarna ulazna slika automatski preuzima set stilskih karakteristika koji se prenose putem algoritma neuronskog stila, omogućavajući na ovaj način porast pamtljivosti celokupne slike. Efikasnost predloženog pristupa evaluirana je eksperimentalno uz izvedbu korisničke studije

Multimodal sentiment analysis in real-life videos

This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target. The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far. This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level. The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated. A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above. The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

The impact of reviews on consumers’ consideration towards electric vehicles (EV)

The development of the Internet and technology led to increasingly more digital consumers, who are tired of being marketed to. Thus, many companies started adopting reviews as part of their marketing strategy to reach the target audience in social media. This trend is also present in the market of electric vehicle (EV). This dissertation was developed with the aim of understanding how EV consumers perceive social media online posts as endorsers of EV as a mobility product, through the lens of the Source Credibility Model. These objectives were addressed using a quantitative research method that adopted an experiment between subjects, comparing firm-created reviews (firmcreated content) with user-created reviews (user-generated content) . Previous literature was reviewed, and an online questionnaire was conducted, with 243 obtained valid answers. Moreover, the willingness to consider and willingness to buy an EV were considered as a variable in the analysis, being proposed because of trustworthiness communicated by the type of review. The results of this dissertation found that the difference between User-Generated Content and car brand reviews (firm-created content) is not statistically significant in the moment of influencing decision of considering or buying an EV as a mobility product. It was observed that there is a valid positive influence relationship of trustworthiness on the relationship between the types of review and the consideration of buying an EV. Lastly, with this model and this research, it was confirmed that there is a positive influence of the trustworthiness on willingness to buy and consideration to buy.A emergência da Internet e o desenvolvimento da tecnologia levou a um número crescente de consumidores digitais, cansados de serem comercializados. Assim, muitas empresas começaram a adotar o marketing de publicação em linha para chegar às pessoas nas redes sociais. Esta nova abordagem do marketing é também utilizada como uma ferramenta no marketing social para promover a mudança de comportamento, especialmente quanto ao processo de decisão de aquisição de um novo veículo elétrico (VE). Esta dissertação foi desenvolvida com o objetivo de compreender como os consumidores de VE veem os anúncios online nas redes sociais como endossantes de VE como um produto de mobilidade, partindo do Modelos de Credibilidade na Fonte. Estes objectivos foram abordados utilizando um método de investigação quantitativa que adoptou uma experiência entre sujeitos, comparando conteúdo criado por empresas, com conteúdo criado por utilizadores online. A literatura anterior foi revista e foi conduzido um questionário online, com 243 respostas bem-sucedidas. Além disso, a vontade de considerar foi incluída como uma variável na análise com influência direta na fiabilidade, dificilmente abordada na literatura. Os resultados constataram que a diferença entre o Conteúdo Gerado pelo Utilizador e o conteúdo de marcas de automóveis não é estatisticamente significativa, no momento de influenciar a decisão de considerar um VE como um produto de mobilidade. Observou-se que existe uma relação de influência positiva válida de confiança na relação entre os tipos de revisão e a consideração da compra. Finalmente, foi confirmado que existe uma influência positiva da fiablilidade na vontade de comprar