RecSysTV

RecSysTV is a series of workshops associated with the ACM RecSys conferences. It aims to publicize recommendations research around the theme of TV/Video.

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Difficult question, what about advertising in TV? And our panel of expert is giving interesting insights

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Our panel is discussing the balance between human and algorithmic crated content on TV

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We have @comcast @verizon @netflix and @Layer6AI in our panel - the future and present of #TV #recsys2016

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Panel session #RecSysTV

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Final session of #RecSysTV about to kick off, with the theme Future Research Directions for TV and Video Recommendations

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"Can you tell us how many explosiones are there?" - generating metadata for video means you have to do a lot of real-time feature extraction

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Here is an example on how creators, programmers, or consumers can use metadata #recsys20

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Last talk this session is Faisal Ishtiaq with his talk on How to generate accurate meta-data for content-based recommendations #RecSysTV

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Accuracy, diversity, Novelty and serendipity - good to see evaluations beyond accuracy at #recsys2016

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Miguel Costa talks about feature engineering for TV recommendations - check out all the signals they use #recsys2016

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Another packed session at #RecSysTV #recsys2016

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We're back from lunch. Beaming in directly from Portugal we have Miguel Costa with two talks #RecSysTV

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When doing #recsys for news, journalists are your most valuable allies because they really care about true content - @sbourke at #recsys2016

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Atom stories can be built from different metadata pieces, and we can use #recsys to build them - @sbourke #recsys2016

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It is a great time to create content, but monetization is complex - @sbourke from @SchibstedGroup

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Content Quality Vs. Monetisation. How do you find the balance? #RecSysTV

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Next up we have Steven Bourke from Schibsted Media Group. He is going to walk us through content curation tools for media. #RecSysTV

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News are moving from text to video - @sbourke talks about media and #recsys @SchibstedGroup #recsys2016

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Using #deeplearning @Layer6AI obtain more accurate models, less complex feature spaces, and is able to have many more inputs for #recsys2016

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Real time updating of model by recalculating with one forward pass through the network with each new interaction. #RecSysTV #DeepLearning