@inproceedings{ubo_mods_00157832,
  author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen},
  title = {In-Store Augmented Reality-Enabled Product Comparison and Recommendation},
  booktitle = {14th ACM Conference on Recommender Systems},
  year = {2020},
  publisher = {Association for Computing Machinery (ACM)},
  address = {New York},
  pages = {180–189},
  keywords = {recommender systems},
  isbn = {9781450375832},
  doi = {10.1145/3383313.3412266},
  abstract = {We present an approach combining the AR-based presentation of product attributes in a physical retail store with recommendations for items only available online. The system supports users’ decision-making process by offering functions for comparing product features between items, both physical and online, and by providing recommendations based on selecting in-store products. The physical products may thus serve as anchors for forming the user’s preferences, also offering a richer and more engaging experience when exploring the products hands-on. Both objective product attributes as well as the visual appearance of a physical product are employed for generating recommendations from the online space. In this way, the advantages of online and in-store shopping can be combined, creating novel multi-channel opportunities for businesses. An empirical evaluation showed that the comparison and recommendation functions were appreciated by users, and hinted some possible benefits of a hybrid physical-online shopping support system. Despite the limitations of the study, there is sufficient evidence to consider this a viable approach worth to be further explored.}
}


@inproceedings{ubo_mods_00139553,
  author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen},
  title = {Augmented-Reality-Enhanced Product Comparison in Physical Retailing},
  booktitle = {Mensch und Computer 2019: Tagungsband},
  year = {2019},
  publisher = {ACM Press},
  address = {New York},
  pages = {55–65},
  keywords = {natural interaction},
  isbn = {978-1-4503-7198-8},
  doi = {10.1145/3340764.3340800},
  abstract = {Augmented reality technology has experienced great improvement in recent years and it has been successfully applied to industry and entertainment settings. However, its application in everyday contexts such as shopping is still very limited. One of the requirements to seamlessly incorporate augmented reality into everyday tasks is to find intuitive, natural methods to make use of it. Due to the inherent capabilities of augmented reality to work as a visual aid to explore and extend the knowledge a user has of the surroundings, this paper proposes the combination of AR technology and product advisors in a novel approach for product comparison. The user’s awareness of the differences between multiple physically present objects is enhanced through virtual augmentations, supporting an intuitive way of comparing two or more products while shopping. To assess the validity of the concept, a prototype for an AR-based shopping assistant for comparing vacuum cleaners has been implemented and evaluated in a user study, testing different methods of visual comparison and interaction.}
}


@article{ubo_mods_00103875,
  author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen},
  title = {Negotiation and Reconciliation of Preferences in a Group Recommender System},
  journal = {Journal of Information Processing},
  year = {2018},
  publisher = {Information Processing Society of Japan},
  volume = {26},
  pages = {186–200},
  keywords = {decision-making},
  issn = {1882-6652},
  doi = {10.2197/ipsjjip.26.186},
  abstract = {This article presents an approach to group recommender systems that focuses its attention on the group’s social interaction during the formulation, discussion and negotiation of the features the item to be jointly selected should possess. Current group recommender techniques are mainly based on aggregating existing user profiles or on a profile of the group as a whole. Our method supports collaborative preference elicitation and negotiation process where desired features have to be chosen individually, but group consensus is needed for them to become active in the item filtering process. Users provide feedback on the selected preferences and change their significance, bringing up new recommendations each time individual settings are modified. The last stage in the decision process is also supported, when users collectively select the final item from the recommendation set. We explored the possible benefits of our approach through the development of three prototypes, each based on a different variant of the approach with a different emphasis on private and group-wide preference spaces. They were evaluated with user groups of different size, addressing questions regarding the effectiveness of different information sharing methods and the repercussion of group size in the recommendation process. We compare the different methods and consolidate the findings in an initial model of recommending for group.}
}