Zobrazeno 1 - 10 of 24 pro vyhledávání: '"Benedikt Loepp"'
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Akademický článek
Multi-list interfaces for recommender systems: survey and future directions
Autor: Benedikt Loepp
Publikováno v: Frontiers in Big Data, Vol 6 (2023)
For a long time, recommender systems presented their results in the form of simple item lists. In recent years, however, multi-list interfaces have become the de-facto standard in industry, presenting users with numerous collections of recommendation
Externí odkaz: https://doaj.org/article/5ab7b2ea348d45cd80702febf1505a7a
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3
Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components
Autor: Timm Kleemann, Jürgen Ziegler, Benedikt Loepp, Magdalena Wagner
Publikováno v: Mensch und Computer
A variety of methods is used nowadays to reduce the complexity of product search on e-commerce platforms, allowing users, for example, to specify exactly the features a product should have, but also, just to follow the recommendations automatically g
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74009dc18aadc0bb6727e444d9dd3a37
https://doi.org/10.1145/3473856.3473859
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4
On the Use of Feature-based Collaborative Explanations : An Empirical Comparison of Explanation Styles
Autor: Benedikt Loepp, Sidra Naveed, Jürgen Ziegler
Publikováno v: UMAP (Adjunct Publication)
Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49eac7a8f1d40d4b7054512e46ae5fba
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85089277702
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6
Towards interactive recommending in model-based collaborative filtering systems
Autor: Jürgen Ziegler, Benedikt Loepp
Publikováno v: RecSys
Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today's systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases h
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7e88fee9dcee225c0a3c11023e5b4ba
https://doi.org/10.1145/3298689.3346949
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8
Interactive recommending with Tag-Enhanced Matrix Factorization (TagMF)
Autor: Timm Kleemann, Jürgen Ziegler, Benedikt Loepp, Tim Donkers
We introduce TagMF, a model-based Collaborative Filtering method that aims at increasing transparency and offering richer interaction possibilities in current Recommender Systems. Model-based Collaborative Filtering is currently the most popular meth
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3faaf6696f8dd4c732502824f8d1ce2c
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85047242844
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9
Impact of consuming suggested items on the assessment of recommendations in user studies on recommender systems
Autor: Benedikt Loepp, Tim Donkers, Jürgen Ziegler, Timm Kleemann
Publikováno v: IJCAI
User studies are increasingly considered important in research on recommender systems. Although participants typically cannot consume any of the recommended items, they are often asked to assess the quality of recommendations and of other aspects rel
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2aa8c78c4b6052ffe425ab43216f4ad7
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85074953001
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