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pro vyhledávání: '"ZANKER, Markus"'
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research efforts tar
Externí odkaz:
http://arxiv.org/abs/2411.16645
Publikováno v:
ACM Trans. Recomm. Syst. 2, 2, Article 17 (June 2024), 34 pages
Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS lit
Externí odkaz:
http://arxiv.org/abs/2410.01822
Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery t
Externí odkaz:
http://arxiv.org/abs/2409.10271
Autor:
Jannach, Dietmar, Zanker, Markus
Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage se
Externí odkaz:
http://arxiv.org/abs/2406.16350
Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the dia
Externí odkaz:
http://arxiv.org/abs/2102.04214
Publikováno v:
Data & Knowledge Engineering Volume 122, July 2019, Pages 142-158 https://www.sciencedirect.com/science/article/pii/S0169023X1830332X
Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. However, up to now m
Externí odkaz:
http://arxiv.org/abs/1907.11000
Publikováno v:
In Smart Agricultural Technology February 2023 3
Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Largely left unexplored, however, is the issue to what extent the descriptives of rating distributions influence
Externí odkaz:
http://arxiv.org/abs/1805.11537
Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-b
Externí odkaz:
http://arxiv.org/abs/1805.00977
Publikováno v:
In Expert Systems With Applications 1 March 2022 189