Weighted faceted browsing for characteristics-based visualization selection through end users

Voigt, Martin; Werstler, Artur; Polowinski, Jan; Meißner, Klaus

doi:10.1145/2305484.2305509

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Also, it has been criticized that faceted search systems often only allow for conjunctive queries and consider all facets equally important for the user [26,28,30,32]. Furthermore, most approaches perform an exact matching to determine the result set.…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

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Blended Recommending

Loepp

Herrmanny

Ziegler

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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We present a novel approach that integrates algorithmic recommender techniques with interactive faceted filtering methods. We refer to this approach as blended recommending. It allows users to interact with a set of filter facets representing criteria that can serve as input for different recommendation methods including both collaborative and content-based filtering. Users can select filter criteria from these facets and weight them to express their preferences and to exert control over the hybrid recommendation process. In contrast to hard Boolean filtering, the method aggregates the weighted criteria and calculates a ranked list of recommendations that is visualized and immediately updated when users change the filter settings. Based on this approach, we implemented an interactive movie recommender, MyMovieMixer. In a user study, we compared the system with a conventional faceted filtering system that served as a baseline to obtain insights into user interaction behavior and to assess recommendation quality for our system. The results indicate, among other findings, a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.

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Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

“…VizBoard [32] is one of the few systems that allow users to change the influence of the selected criteria on the result set. The authors emphasize the importance of prioritizing facets to order the results appropriately, and to avoid excluding relevant items.…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

Blended Recommending

Loepp

Herrmanny

Ziegler

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…The idea of using faceted search techniques to filter though a multi-dimensional set of data has been well explored [8,11,28,30,33] and applied to finding items in many domains including research publications [13], webpages [4], ecommerce [29], image search [32,34], and to a lesser extent, video collections [5,31].…”

Section: Faceted Searchmentioning

confidence: 99%

Video lens

Matejka

Grossman

Fitzmaurice

2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

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Figure 1. Overview of the Video Lens interface consisting of a video player in the top left surrounded by the Multi-Attribute Grid, Single Attribute Controllers, and Video Timelines which support the faceted search and selection of video events based on a multidimensional set of timeline-based metadata. ABSTRACTWe present Video Lens, a framework which allows users to visualize and interactively explore large collections of videos and associated metadata. The primary goal of the framework is to let users quickly find relevant sections within the videos and play them back in rapid succession. The individual UI elements are linked and highly interactive, supporting a faceted search paradigm and encouraging exploration of the data set. We demonstrate the capabilities and specific scenarios of Video Lens within the domain of professional baseball videos. A user study with 12 participants indicates that Video Lens efficiently supports a diverse range of powerful yet desirable video query tasks, while a series of interviews with professionals in the field demonstrates the framework's benefits and future potential.

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“…But, using facets means that all selected facet terms are mandatory and equally weighted. Selecting less important facet terms may remove potentially interesting products unintentionally, while specifying only a few criteria may lead to a too large result set where interesting alternatives are not obvious [22].…”

Section: Introductionmentioning

confidence: 99%

“…One possibility to give users control over recommendation and ranking is to let them weight terms according to their preferences (e.g. [11,22]). From a user perspective, these systems have been evaluated as very helpful, and they are able to increase user satisfaction (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a search interface for preference-based ranking

Kern

Hoek²,

Hienert

2018

Proceedings of the 10th Nordic Conference on Human-Computer Interaction

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Finding a product online can be a challenging task for users. Faceted search interfaces, often in combination with recommenders, can support users in finding a product that fits their preferences. However, those preferences are not always equally weighted: some might be more important to a user than others (e.g. red is the favorite color, but blue is also fine) and sometimes preferences are even contradictory (e.g. the lowest price vs. the highest performance). Often, there is even no product that meets all preferences. In those cases, faceted search interfaces reach their limits. In our research, we investigate the potential of a search interface, which allows a preference-based ranking based on weighted search and facet terms. We performed a user study with 24 participants and measured user satisfaction and system performance. The results show that with the preference-based search interface the users were given more alternatives that best meet their preferences and that they are more satisfied with the selected product than with a search interface using standard facets. Furthermore, in this work we study the relationship between user satisfaction and search precision within the whole search session and found first indications that there might be a relation between them.

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Weighted faceted browsing for characteristics-based visualization selection through end users

Cited by 9 publications

References 19 publications

Blended Recommending

Blended Recommending

Video lens

Evaluation of a search interface for preference-based ranking

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