User effort vs. accuracy in rating-based elicitation

Cremonesi, Paolo; Garzottto, Franca; Turrin, Roberto

doi:10.1145/2365952.2365963

Cited by 29 publications

(9 citation statements)

References 26 publications

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“…& the system makes recommendations based on the user ratings [26]; & the user is new and the system does not have enough information to build the user profile and generate personalized recommendations; & a multimedia item is new and no user has already rated that item, which may result in the system to be unable to accurately recommend that item to any users. Solution: The system invites the user to rate a set of multimedia items that are selected as the most informative to reveal the preferences of the user to the system.…”

Section: Solutionmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

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Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of Bgood^design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (Brecommendations^) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, Multimed Tools Appl

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Section: Solutionmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

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“…Many recommenders take explicit feedback from their users to form the basis of the recommendations [23], or to set up an initial context for new users [5,17,24]. Relevance feedback incorporates user responses -either explicit or implicit -into a feedback loop to improve future iterations of the recommender [1,13].…”

Section: Background and Related Workmentioning

confidence: 99%

Letting Users Choose Recommender Algorithms

Ekstrand

Kluver

Harper

et al. 2015

Proceedings of the 9th ACM Conference on Recommender Systems

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Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.

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“…The latent space tries to explain ratings by characterizing both items and users on factors automatically inferred from user feedback. Examples of these algorithms, are: Singular Value Decomposition (SVD) (Ricci et al, 2011) and its variations (PureSVD and AsySVD (Cremonesi et al, 2012b)), Probabilistic Matrix factorization , Rankingbased Matrix factorization , Regularized Kernel Matrix Factorization (Rendle and SchmidtThieme, 2008), and so on. 3.…”

Section: Collaborative Filtering Algorithmsmentioning

confidence: 99%