2021

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  Nießner, J. & Ludwig, T. (2021)Design of a Knowledge-Based Recommender System for Recipes From an End-User Perspective

  Mensch und Computer 2021. New York, NY, USA, Publisher: Association for Computing Machinery, Pages: 512–519 doi:10.1145/3473856.3473888
  [BibTeX] [Abstract] [Download PDF]

  Nowadays, recommender systems are a fundamental part of several online services. However, most of these systems rely on collective user data and ratings or a preselection of parameters to derive appropriate recommendations. Within this paper, we examine recommendations without previous user data. We therefore designed and evaluated a knowledge-based recommender system by turning to recipe recommendations that offer alternatives for favorite recipes. We introduce and compare three versions of a given algorithm. Our evaluation shows that the knowledge-based approach may serve as a good start for deriving appropriate recommendations without prior user data. Moreover, we show that end-users’ assumptions about decisive criteria of a recommender system do not necessarily match the later actual decisive criteria.

  @inproceedings{niesner_design_2021,
  address = {New York, NY, USA},
  series = {{MuC} '21},
  title = {Design of a {Knowledge}-{Based} {Recommender} {System} for {Recipes} {From} an {End}-{User} {Perspective}},
  isbn = {978-1-4503-8645-6},
  url = {https://doi.org/10.1145/3473856.3473888},
  doi = {10.1145/3473856.3473888},
  abstract = {Nowadays, recommender systems are a fundamental part of several online services. However, most of these systems rely on collective user data and ratings or a preselection of parameters to derive appropriate recommendations. Within this paper, we examine recommendations without previous user data. We therefore designed and evaluated a knowledge-based recommender system by turning to recipe recommendations that offer alternatives for favorite recipes. We introduce and compare three versions of a given algorithm. Our evaluation shows that the knowledge-based approach may serve as a good start for deriving appropriate recommendations without prior user data. Moreover, we show that end-users’ assumptions about decisive criteria of a recommender system do not necessarily match the later actual decisive criteria.},
  urldate = {2021-09-14},
  booktitle = {Mensch und {Computer} 2021},
  publisher = {Association for Computing Machinery},
  author = {Nießner, Julia and Ludwig, Thomas},
  month = sep,
  year = {2021},
  keywords = {Knowledge-based Filtering, Recipes, Recommender System, Similarity Metrics, User Study},
  pages = {512--519},
  }

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  Weber, P., Krings, K., Nießner, J., Brodesser, S. & Ludwig, T. (2021)FoodChattAR: Exploring the Design Space of Edible Virtual Agents for Human-Food Interaction

  Designing Interactive Systems Conference 2021. New York, NY, USA, Publisher: Association for Computing Machinery, Pages: 638–650 doi:10.1145/3461778.3461998
  [BibTeX] [Abstract] [Download PDF]

  There has been recent criticism from researchers towards simple replication of traditional role models in the design of virtual agents and robots, and a call for new forms of interaction and communication with technology. By exploring the field of Human-Food interaction (HFI) – a sub-area of Human-Computer Interaction (HCI) which aims to investigate the diversity of ways people interact with food – we therefore specifically examine the design space of edible anthropomorphic virtual agents (EAVAs). To understand human-to-food interactive communication, we conducted an interview study with 19 participants, followed by a co-design workshop on the design of conversational agents for personified food. Based on the results, we implemented a prototype called FoodChattAR that employs augmented reality and chatbots to interact and communicate with food. Our evaluation with 21 participants shows that FoodChattAR turns eating into fun, while at the same time the food conveys relevant societal facts about itself. We contribute to the field of HCI by introducing EAVAs as a novel human-to-food interaction.

  @inproceedings{weber_foodchattar_2021,
  address = {New York, NY, USA},
  series = {{DIS} '21},
  title = {{FoodChattAR}: {Exploring} the {Design} {Space} of {Edible} {Virtual} {Agents} for {Human}-{Food} {Interaction}},
  isbn = {978-1-4503-8476-6},
  shorttitle = {{FoodChattAR}},
  url = {https://doi.org/10.1145/3461778.3461998},
  doi = {10.1145/3461778.3461998},
  abstract = {There has been recent criticism from researchers towards simple replication of traditional role models in the design of virtual agents and robots, and a call for new forms of interaction and communication with technology. By exploring the field of Human-Food interaction (HFI) – a sub-area of Human-Computer Interaction (HCI) which aims to investigate the diversity of ways people interact with food – we therefore specifically examine the design space of edible anthropomorphic virtual agents (EAVAs). To understand human-to-food interactive communication, we conducted an interview study with 19 participants, followed by a co-design workshop on the design of conversational agents for personified food. Based on the results, we implemented a prototype called FoodChattAR that employs augmented reality and chatbots to interact and communicate with food. Our evaluation with 21 participants shows that FoodChattAR turns eating into fun, while at the same time the food conveys relevant societal facts about itself. We contribute to the field of HCI by introducing EAVAs as a novel human-to-food interaction.},
  urldate = {2021-07-05},
  booktitle = {Designing {Interactive} {Systems} {Conference} 2021},
  publisher = {Association for Computing Machinery},
  author = {Weber, Philip and Krings, Kevin and Nießner, Julia and Brodesser, Sabrina and Ludwig, Thomas},
  month = jun,
  year = {2021},
  keywords = {Human-Food Interaction, rendezfood, Anthropomorphism, Augmented Food, Conversational Agents, Edible Anthropomorphic Virtual Agents, Virtual Agents},
  pages = {638--650},
  }