Just Give Me What I Want

Hosey, Christine; Vujovic, Lara; Thomas, B.S.; Garcia-Gathright, Jean; Thom, Jennifer S.

doi:10.1145/3290605.3300529

Cited by 23 publications

(7 citation statements)

References 38 publications

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“…A number of studies have explored user behavior when searching for specific entities such as music tracks, products, and books [7,23,27,32]. While focused searches have the goal of finding a specific entity, non-focused searches involve broader intents, where the user is in an exploratory mindset [33,51,55].…”

Section: Entity Searchmentioning

confidence: 99%

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Improving Content Retrievability in Search with Controllable Query Generation

Penha

Palumbo²,

Aziz

et al. 2023

Proceedings of the ACM Web Conference 2023

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An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search "harry potter". The amount of broad queries where users want to discover new entities, e.g. in music search "chill lyrical electronica with an atmospheric feeling to it", and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the retrievability of the entities (I) the training data used for dense retrieval models and (II) the distribution of narrow and broad intent queries issued in the system. We propose CtrlQGen, a method that generates queries for a chosen underlying intentnarrow or broad. We can use CtrlQGen to improve factor (I) by generating training data for dense retrieval models comprised of diverse synthetic queries. CtrlQGen can also be used to deal with factor (II) by suggesting queries with broader intents to users. Our results on datasets from the domains of music, podcasts, and books reveal that we can significantly decrease the retrievability bias of a dense retrieval model when using CtrlQGen. First, by using the generated queries as training data for dense models we make 9% of the entities retrievable-go from zero to non-zero retrievability. Second, by suggesting broader queries to users, we can make 12% of the entities retrievable in the best case.

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Section: Entity Searchmentioning

confidence: 99%

“…This type of search based on bibliographic data (e.g. title, artist, author, etc) to find entities [7] has been referred to as narrow intent queries [27]. However, user information needs are diverse and can be more complex depending on their current mindset [33].…”

Section: Introductionmentioning

confidence: 99%

Improving Content Retrievability in Search with Controllable Query Generation

Penha

Palumbo²,

Aziz

et al. 2023

Proceedings of the ACM Web Conference 2023

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“…Somebody who is openly exploring a music collection or who follows an artist determines success in a much wider context and is therefore willing to contribute extra effort. Using the example of 'artist search', Hosey et al (2019) provide design recommendations for mobile search interfaces for a leisure-related music experience and suggest a division of the mobile screen space based on different mindsets. The top of the screen that is mostly used by focused users would benefit from a query auto-suggestion feature (as such users hardly ever use SERPs) to provide an artist link and known songs or albums from that artist.…”

Section: Searching For Leisurementioning

confidence: 99%

“…Overall, this section mostly shows examples and studies that have investigated a) users engaging in leisure information behavior to learn about their requirements and preferences (e.g., Elsweiler et al (2011)) or b) the study of existing search interfaces from a leisure context to develop design guidelines (e.g., Hosey et al (2019)). In addition to this, some areas of IR also cover research that relates to leisure application contexts, such as Multimedia IR (Soleymani et al, 2017) or Lifelogging (Gurrin et al, 2014).…”

Section: Searching For Leisurementioning

confidence: 99%

Search Interface Design and Evaluation

Liu

et al. 2021

FNT in Information Retrieval

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This monograph reviews research on the design and evaluation of search user interfaces that has been published within the past 10 years. Our primary goal is to integrate state-of-the-art research in the areas of information seeking behavior, information retrieval, and human-computer interaction on the topic of search interface. Specifically, this monograph (1) describes the history and background of the development of the search interface; (2) introduces information search behavior models that help conceptualize users' information needs, and how people seek, select, and use information; (3) characterizes the major components of search interfaces that support different subprocesses based on Marchonini's information seeking process model; (4) reviews the design of search interfaces for different user groups, especially that of vulnerable people, as well as personalized and adaptive search interfaces; (5) identifies evaluation methods of search interfaces and how they were implemented in research having different evaluation purposes. We also provide an outlook on the future trends of search interfaces including

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“…First, we expand upon literature concerning software updates [52,53,22] by critically scrutinising their strategic role in producing new norms of use aimed at diminishing user agency and exerting control over user behaviours. Second, we contribute to debates around how technology influences the way in which people listen to, search for, and curate music [9,28,39] by showing that music streaming services function as sites where ongoing cultural struggles, tensions, and negotiations regarding ownership of cultural content and digital technologies take place.…”

Section: Introductionmentioning

confidence: 99%

"My Library Has Just Been Obliterated": Producing New Norms of Use Via Software Update

Morreale

Eriksson

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Software updates are commonly perceived as tools for fixing flaws and improving functionality. In this paper, we problematise this view by showing how software updates may also be used by vendors to create new norms of use that control user behaviour and reduce their agency. We explore the nature and aftermath of a controversial software update that was released by Spotify in June 2019. By analysing almost 3,500 reactions to this update, we show how it removed and modified several features in ways that severely affected users' capability to organise, navigate, and maintain their music libraries, while it pushed modes of listening that delegate song selection to Spotify. Elaborating upon our results, we discuss how updates may be used as political tools that privilege certain forms of behaviour while restricting others. We also portray updates as sites where ongoing struggles and negotiations regarding user agency and digital ownership take place.

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Just Give Me What I Want

Cited by 23 publications

References 38 publications

Improving Content Retrievability in Search with Controllable Query Generation

Improving Content Retrievability in Search with Controllable Query Generation

Search Interface Design and Evaluation

"My Library Has Just Been Obliterated": Producing New Norms of Use Via Software Update

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