The Tag Genome Dataset for Books

Attaching tags to items, such as books or movies, is found in many online systems. While a majority of these systems use binary tags, continuous item-tag relevance scores, such as those in tag genome, offer richer descriptions of item content. For example, tag genome for movies assigns the tag "gangster" to the movie "The Godfather (1972)" with a score of 0.93 on a scale of 0 to 1. Tag genome has received considerable attention in recommender systems research and has been used in a wide variety of studies, from investigating the effects of recommender systems on users to generating ideas for movies that appeal to certain user groups.In this paper, we present tag genome for books, a dataset containing book-tag relevance scores, where a significant number of tags overlap with those from tag genome for movies. To generate our dataset, we designed a survey based on popular books and tags from the Goodreads dataset. In our survey, we asked users to provide ratings for how well tags applied to books. We generated book-tag relevance scores based on user ratings along with features from the Goodreads dataset. In addition to being used to create book recommender systems, tag genome for books can be combined with the tag genome for movies to tackle cross-domain problems, such as recommending books based on movie preferences.Peer reviewe

Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page

Rating consistency is consistently underrated : An exploratory analysis of movie-tag rating inconsistency

Publisher Copyright: © 2022 ACM.Content-based and hybrid recommender systems rely on item-tag ratings to make recommendations. An example of an item-tag rating is the degree to which the tag "comedy"applies to the movie "Back to the Future (1985)". Ratings are often generated by human annotators who can be inconsistent with one another. However, many recommender systems take item-tag ratings at face value, assuming them all to be equally valid. In this paper, we investigate the inconsistency of item-tag ratings together with contextual factors that could affect consistency in the movie domain. We conducted semi-structured interviews to identify potential reasons for rating inconsistency. Next, we used these reasons to design a survey, which we ran on Amazon Mechanical Turk. We collected 6,070 ratings from 665 annotators across 142 movies and 80 tags. Our analysis shows that ∼45% of ratings are inconsistent with the mode rating for a given movie-tag pair. We found that the single most important factor for rating inconsistency is the annotator's perceived ease of rating, suggesting that annotators are at least tacitly aware of the quality of their own ratings. We also found that subjective tags (e.g. "funny", "boring") are more inconsistent than objective tags (e.g. "robots", "aliens"), and are associated with lower tag familiarity and lower perceived ease of rating.Peer reviewe

Mensch-Computer-Interaktion als zentrales Gebiet der Informatik – Bestandsaufnahme, Trends und Herausforderungen

Mensch-Computer-Interaktion (MCI) beschäftigt sich mit Fragen rund um die benutzer- und kontextegerechte Gestaltung von IT-Systemen. Ohne MCI ist die vielbeschworene digitale Transformation nicht möglich, da Systeme, die nicht benutzbar (gebrauchstauglich) sind, für ihre Nutzer wertlos oder sogar gefährlich sind – erst Nutzbarkeit schafft Nutzen! In diesem Beitrag sammeln wir einige Beispiele dafür, wo und wie MCI in der Entwicklung zukünftiger IT-Systeme relevant ist – von nutzerzentrierter künstlicher Intelligenz über benutzbare Sicherheit, cyberphysische Systeme und digital Arbeit hin zu Augmented Reality und Virtual Reality