@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}

}

@article{Rosén2016,

title = {The enrichment of lexical resources through incremental parsebanking},

author = {Victoria Rosén and Martha Thunes and Petter Haugereid and Gyri S. Losnegaard and Helge Dyvik and Paul Meurer and Gunn Lyse and Koenraad De Smedt},

url = {https://bora.uib.no/bora-xmlui/handle/1956/15680},

year  = {2016},

date = {2016-06-01},

urldate = {2016-06-01},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{Tobias2016,

title = {Alleviating the new user problem in collaborative filtering by exploiting personality information},

author = {Ignacio Fernandez Tobias and Matthias Braunhofer and Mehdi Elahi and Francesco Ricci and Ivan Cantador},

url = {https://www.researchgate.net/publication/285574429_Alleviating_the_New_User_Problem_in_Collaborative_Filtering_by_Exploiting_Personality_Information},

doi = {10.1007/s11257-016-9172-z},

year  = {2016},

date = {2016-06-01},

journal = {User Modeling and User-Adapted Interaction},

volume = {26},

number = {2-3},

pages = {221-255},

abstract = {The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.},

note = {Pre SFI},

keywords = {Active Learning, Cold-start, Collaborative filtering, Cross-domain, Recommender systems, User Personality, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@article{Wozniak2016,

title = { RAMPARTS: Supporting sensemaking with spatially-aware mobile interactions},

author = {Pawel Wozniak and Nitesh Goyal and Przemyslaw Kucharski and Lars Lischke and Sven Mayer and Morten Fjeld},

url = {https://dl.acm.org/doi/10.1145/2858036.2858491

https://www.youtube.com/watch?v=t01yLj3xhVc},

year  = {2016},

date = {2016-05-01},

urldate = {2016-05-01},

note = {Pre SFI},

keywords = {WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {article}

}

@article{Mutlu2016,

title = {VizRec: Recommending Personalized Visualizations},

author = {Belgin Mutlu and Eduardo Veas and Christoph Trattner},

url = {https://www.christophtrattner.info/pubs/ACM-TIIS.pdf},

year  = {2016},

date = {2016-01-01},

journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},

volume = {6},

number = {4},

pages = {1-40},

abstract = {Visualizations have a distinctive advantage when dealing with the information overload problem: since they

are grounded in basic visual cognition, many people understand them. However, creating the appropriate

representation requires specific expertise of the domain and underlying data. Our quest in this paper is to

study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has

to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a 

visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects

of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can

we capture and use those aspects to recommend visualizations? This paper investigates strategies to

 recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to

estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts

are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines

information on what a chart is about (tags) and how good it is (ratings). We present the design principles

behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a

crowd sourced study, and the analysis of strategies for visualization recommendation.},

note = {Pre SFI},

keywords = {CCS Concepts, Collaborative filtering;, Content ranking, Human-centered computing, Information Systems, Personalization},

pubstate = {published},

tppubtype = {article}

}

@article{Borch2015b,

title = {Mediasync Report 2015: Evaluating timed playback of HTML5 Media},

author = {Njål Borch and Ingar Mæhlum Arntzen},

url = {https://norceresearch.brage.unit.no/norceresearch-xmlui/bitstream/handle/11250/2711974/Norut_Tromso_rapport_28-2015.pdf?sequence=2&isAllowed=y},

isbn = {978-82-7492-319-5},

year  = {2015},

date = {2015-12-08},

journal = {Norut},

abstract = {In this report we provide an extensive analysis of timing aspects of HTML5 Media, across a variety of browsers,

operating systems and media formats. Particularly we investigate how playback compares to the progression of

the local clock and how players respond to time-shifting and adjustments in playback-rate.

Additionally, we use the MediaSync JS library to enforce correctly timed playback for HTML5 media, and indicate

the effects this has on user experience. MediaSync is developed based on results from the above analysis.

MediaSync aims to provide a best effort solution that works across a variety of media formats, operating systems

and browser types, and does not make optimizations for specific permutations..

},

note = {Pre SFI},

keywords = {HTML5, MediaSync, WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {article}

}

@article{Meijer2014,

title = {Checking, sharing, clicking and linking: Changing patterns of news use between 2004 and 2014.},

author = {Irene Costera Meijer and Tim Groot Kormelink},

url = {https://www.tandfonline.com/doi/pdf/10.1080/21670811.2014.937149?needAccess=true},

doi = {10.1080/21670811.2014.937149},

issn = { 2167-0811},

year  = {2014},

date = {2014-08-01},

journal = {Digital Journalism},

volume = {3},

number = {5},

pages = {664-679},

abstract = {This paper challenges the generally taken-for-granted automatic link between media platforms, media technology and news user practices. It explores what has changed in people’s news consumption by comparing patterns in news use between 2004–2005 and 2011–2014. While new, social and mobile media technologies did not unleash a revolution in people’s dealings with news, they have facilitated, deepened and broadened user practices we already found in 2004–2005: monitoring, checking, snacking, scanning, watching, viewing, reading, listening, searching and clicking. In addition, these forms of news usage appear to increasingly order, control, organize and anchor other practices and the experience of time and environment in which they occur. Meanwhile, new and mobile news practices like linking, sharing, liking, recommending, commenting and voting have not become as central to news consumption as often assumed.},

note = {Pre SFI},

keywords = {Audience Studies, Comparative Qualitative Research, Creative Mixed Methodology, Digitalization, News Consumption, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Velldal2012,

title = {Speculation and negation: Rules, rankers, and the role of syntax},

author = {Erik Velldal and Lilja Øvrelid and Jonathon Read and Stephan Oepen},

url = {https://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00126},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}