@article{Hazrati2020,

title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},

author = {Naieme Hazrati and Mehdi Elahi},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},

doi = {https://doi.org/10.1111/exsy.12645},

year  = {2020},

date = {2020-10-19},

journal = {Expert Systems},

volume = {e12645},

pages = {1-20},

abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},

note = {Pre SFI},

keywords = {Cold Start, Multimedia, New Item, Recommender systems, Visually Aware, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@article{Boididou2017,

title = {Verifying information with multimedia content on twitter: A comparative study of automated approaches},

author = {Christina Boididou and Stuart Middleton and Zhiwei Jin and Symeon Papadopoulos and Duc-Tien Dang-Nguyen and G. Boato and Ioannis (Yiannis) Kompatsiaris},

url = {https://www.researchgate.net/publication/319859894_Verifying_information_with_multimedia_content_on_twitter_A_comparative_study_of_automated_approaches},

doi = {10.1007/s11042-017-5132-9},

year  = {2017},

date = {2017-09-01},

urldate = {2017-09-01},

journal = {Multimedia Tools and Applications},

volume = {77},

number = {12},

pages = {15545-15571},

abstract = {An increasing amount of posts on social media are used for dissem- inating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental eect on their credibility. To avoid such eects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract

},

note = {Pre SFI},

keywords = {Credibility, Fake Detection, Multimedia, Social Media, Trust, Twitter, Veracity, Verification, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}