Designing Familiar Open Surfaces

While participatory design makes end-users part of the design process, we might also want the resulting system to be open for interpretation, appropriation and change over time to reflect its usage. But how can we design for appropriation? We need to strike a good balance between making the user an active co-constructor of system functionality versus making a too strong, interpretative design that does it all for the user thereby inhibiting their own creative use of the system. Through revisiting five systems in which appropriation has happened both within and outside the intended use, we are going to show how it can be possible to design with open surfaces. These open surfaces have to be such that users can fill them with their own interpretation and content, they should be familiar to the user, resonating with their real world practice and understanding, thereby shaping its use

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

Recently, making recommendations for ephemeral groups which contain dynamic users and few historic interactions have received an increasing number of attention. The main challenge of ephemeral group recommender is how to aggregate individual preferences to represent the group's overall preference. Score aggregation and preference aggregation are two commonly-used methods that adopt hand-craft predefined strategies and data-driven strategies, respectively. However, they neglect to take into account the importance of the individual inherent factors such as personality in the group. In addition, they fail to work well due to a small number of interactive records. To address these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to define the concept of Group Personality. We then use the personality attention mechanism to aggregate group preferences. The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups. The experimental results demonstrate that our model significantly outperforms the state-of-the-art methods w.r.t. the score of both Recall and NDCG on Amazon and Yelp datasets

An exploration of the sustainability of dinner recipes

Masteroppgave i klinisk ernæringNUCLI395MAMD-NUCL

Think You Know Ketchup, Think Again

This project was submitted in partial fulfillment of the requirements for the Master of Science in Journalism degree

Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

Customer interactions with AI: How can Marley Spoon optimize its chatbot performance to improve the touchpoint experience along the customer journey?

The purpose of this in-company project is to identify chatbot optimization recommendations for Marley Spoon to improve the touchpoint experience along the customer journey. Customer interactions with Artificial Intelligence became a relevant part of communication channels within business processes and are already applied in many marketing strategies. Grace to its machine learning capability, chatbots can combine natural language processing and natural language understanding in order to offer an automated customer experience. Nowadays, AI-chatbots are not only able to operate on a mechanical and thinking level, but are also developing on a feeling level. Hence, chatbots can also understand human emotions and adapt empathically to different moods and circumstances. In this way, a well implemented chatbot should not only be used as a simple FAQ machine, but also be implemented for different marketing purposes such as customer attraction and retention. The results of this research are based on a profound literature review with recent articles of well-respected researchers in this field. Moreover, a primary research was conducted in form of in-depth interviews with different specialist of the company and a customer satisfaction survey collected by the chatbot platform. Deriving from the findings of this research, there are three recommendations provided to the company, which should be implemented to improve the touchpoint experience. Those three implementations should be a be a new chatbot interface with more customer engagement, integrating the chatbot to different customer journey stages and setting up a chatbot superteam with specified scope and responsibilities.O objetivo deste projeto em empresa é identificar recomendações de otimização de chatbot para Marley Spoon, de modo a melhorar a experiência touchpoint ao longo da jornada do cliente. As interações dos clientes com a Inteligência Artificial tornaram-se uma parte crucial dos canais de comunicação integradas nos processos de negócios, sendo que já estão a ser aplicadas em muitas estratégias de marketing. Devido à capacidade de aprendizagem, os chatbots podem combinar processamento de linguagem natural com compreensão de linguagem natural, de maneira a oferecer uma experiência automatizada ao cliente. Nos dias de hoje, os AI-chatbots não só são capazes de operar num nível mecânico e de pensamento, mas também estão desenvolvidos a nível de sentimento. Os chatbots podem inclusivamente entender as emoções humanas e adaptar-se efetivamente diferentes estados de espírito e circunstâncias. Desta forma, um chatbot eficiente não deve ser usado apenas como uma simples máquina de resposta a perguntas frequentes, mas também deve ser utilizado para diferentes fins de marketing, como a atração e retenção de clientes. Os resultados da pesquisa foram retirados da análise de conceitos teóricos da literatura científica, focada em artigos recentes de investigadores referenciados nessa área. A pesquisa primária foi realizada em forma de entrevistas com diferentes especialistas da empresa e, também, através de uma pesquisa de satisfação de cliente na plataforma chatbot. Com base nos resultados desta pesquisa, há três recomendações facultadas à empresa, que devem ser implementadas para melhorar a experiência touchpoint. Uma nova interface chatbot com mais comprometimento com o cliente, integrando o chatbot em diferentes estágios da jornada do cliente e configurando uma superteam chatbot com intuito e responsabilidades especificados