Understanding choice overload in recommender systems

Bollen, Dirk; Knijnenburg, Bart P.; Willemsen, Martijn C.; Graus, Mark P.

doi:10.1145/1864708.1864724

Cited by 169 publications

(121 citation statements)

References 18 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Also other works [13,23,24,27,61,77] have pinpointed that system-centric quality might not always correlate with user-centric quality, as the latter may depend on factors that go beyond the characteristics of the recommendation algorithm itself.…”

Section: Related Work 21 Recommender Systemmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

View full text Add to dashboard Cite

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

show abstract

Section: Related Work 21 Recommender Systemmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…To compare the different recommendation algorithms, users in this test were presented with five different lists of eight recommendations each. Eight recommendations is considered as an optimal number to prevent choice overload, while providing users different options and the coupled choice satisfaction [2]. These five lists were randomly shuffled and presented without any hint of the algorithm that was used to produce the list in order to obtain unbiased evaluation results.…”

Section: Comparison Of the Algorithmsmentioning

confidence: 99%

Hybrid group recommendations for a travel service

Pessemier

Dhondt

Martens

2016

Multimed Tools Appl

View full text Add to dashboard Cite

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.

show abstract

“…On the contrary, system-centric evaluation has the advantage to be immediate, economical and easy to perform on several domains and with multiple algorithms. Recently, many researchers have argued that the system-centric evaluation of RSs in e-commerce applications does not always correlate with how the users perceive the value of recommendations [2,5,6,19,22,27]. This may happen because system-centric evaluation cannot reliably measure non-accuracy metrics such as novelty -the extension to which recommendations are perceived as new -which more reflects the user and business dimensions.…”

Section: Introductionmentioning

confidence: 99%

User-Centric vs. System-Centric Evaluation of Recommender Systems

Cremonesi

Garzotto

Turrin³

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Recommender Systems (RSs) aim at helping users search large amounts of contents and identify more effectively the items (products or services) that are likely to be more useful or attractive. The quality of a RS can be defined from two perspectives: system-centric, in which quality measures (e.g., precision, recall) are evaluated using vast datasets of preferences and opinions on items previously collected from users that are not interacting with the RS under study; user-centric, in which user measures are collected from users interacting with the RS under study. Prior research in e-commerce has provided some empirical evidence that system-centric and user-centric quality methods may lead to inconsistent results, e.g., RSs that were "best" according to systemcentric measures were not the top ones according to user-centric measures. The paper investigates if a similar mismatch also exists in the domain of e-tourism. We discuss two studies that have adopted a system-centric approach using data from 210000 users, and a user-centric approach involving 240 users interacting with an online hotel booking service. In both studies, we considered four RSs that employ an implicit user preference elicitation technique and different baseline and state-of-the-art recommendation algorithms. In these four experimental conditions, we compared system-centric quality measures against user-centric evaluation results. System-centric quality measures were consistent with usercentric measures, in contrast with past studies in e-commerce. This pinpoints that the relationship between the two kinds of metrics may depend on the business sector, is more complex that we may expect, and is a challenging issues that deserves further research.

show abstract

Understanding choice overload in recommender systems

Cited by 169 publications

References 18 publications

User interface patterns in recommendation-empowered content intensive multimedia applications

User interface patterns in recommendation-empowered content intensive multimedia applications

Hybrid group recommendations for a travel service

User-Centric vs. System-Centric Evaluation of Recommender Systems

Contact Info

Product

Resources

About