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2021
Dietmar Jannach; Mathias Jesse; Michael Jugovac; Christoph Trattner
Exploring Multi-List User Interfaces for Similar-Item Recommendations Conference
29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21) 2021.
Abstract | BibTeX | Tags: Recommender system, User Interface, User Study | Links:
@conference{Jannach2021,
title = {Exploring Multi-List User Interfaces for Similar-Item Recommendations},
author = {Dietmar Jannach and Mathias Jesse and Michael Jugovac and Christoph Trattner},
url = {https://mediafutures.no/conference_umap_2021-2/},
year = {2021},
date = {2021-03-26},
organization = {29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21)},
abstract = {On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures.},
keywords = {Recommender system, User Interface, User Study},
pubstate = {published},
tppubtype = {conference}
}
2016
Mehdi Elahi; Francesco Ricci; Neil Rubens
A survey of active learning in collaborative filtering recommender systems Journal Article
In: Computer Science Review, vol. 20, pp. 29-50, 2016, (Pre SFI).
Abstract | BibTeX | Tags: Active Learning, Cold Start, Collaborative filtering, New Start, New User, Preference Elicitation, Rating Elicitation, Recommender system, WP2: User Modeling Personalization and Engagement | Links:
@article{Elahi2016,
title = {A survey of active learning in collaborative filtering recommender systems},
author = {Mehdi Elahi and Francesco Ricci and Neil Rubens},
url = {https://reader.elsevier.com/reader/sd/pii/S1574013715300150?token=EA12A462FC07F42733F4F13375217A57D3FDC7F6047C133156CB1F4E4487DF24C5366547DF4530A25942F690233F2E30},
doi = {10.1016/j.cosrev.2016.05.002},
year = {2016},
date = {2016-06-02},
journal = {Computer Science Review},
volume = {20},
pages = {29-50},
abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.},
note = {Pre SFI},
keywords = {Active Learning, Cold Start, Collaborative filtering, New Start, New User, Preference Elicitation, Rating Elicitation, Recommender system, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.
2015
Neil Rubens; Mehdi Elahi; Masashi Sugiyama; Dain Kaplan
Active learning in recommender systems Book Chapter
In: Ricci, Francesco; Rokach, Lior; Shapira, Bracha (Ed.): pp. 809-846, Springer, 2015, ISBN: 978-1-4899-7637-6, (Pre SFI).
Abstract | BibTeX | Tags: Recommender system, WP2: User Modeling Personalization and Engagement | Links:
@inbook{Rubens2015,
title = {Active learning in recommender systems},
author = {Neil Rubens and Mehdi Elahi and Masashi Sugiyama and Dain Kaplan},
editor = {Francesco Ricci and Lior Rokach and Bracha Shapira},
url = {https://link.springer.com/chapter/10.1007/978-1-4899-7637-6_24},
doi = {10.1007/978-1-4899-7637-6_24},
isbn = {978-1-4899-7637-6},
year = {2015},
date = {2015-01-01},
pages = {809-846},
publisher = {Springer},
abstract = {In Recommender Systems (RS), a user’s preferences are expressed in terms of rated items, where incorporating each rating may improve the RS’s predictive accuracy. In addition to a user rating items at-will (a passive process), RSs may also actively elicit the user to rate items, a process known as Active Learning (AL). However, the number of interactions between the RS and the user is still limited. One aim of AL is therefore the selection of items whose ratings are likely to provide the most information about the user’s preferences. In this chapter, we provide an overview of AL within RSs, discuss general objectives and considerations, and then summarize a variety of methods commonly employed. AL methods are categorized based on our interpretation of their primary motivation/goal, and then sub-classified into two commonly classified types, instance-based and model-based, for easier comprehension. We conclude the chapter by outlining ways in which AL methods could be evaluated, and provide a brief summary of methods performance.},
note = {Pre SFI},
keywords = {Recommender system, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {inbook}
}