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Lime recommendation
1.
© Fraunhofer IDMT T.
Köllmer†, E. Berndl‡, T. Weißgerber‡, P. Aichroth† , H. Kosch‡ A Workflow for Cross Media Recommendations based on Linked Data Analysis † Fraunhofer IDMT ‡ University of Passau Thomas Köllmer thomas.koellmer@idmt.fraunhofer.de
2.
© Fraunhofer IDMT
2 The MICO Project http://www.mico-project.eu FP7 STREP, GA# 610480 7 Partners: Salzburg Research, Fraunhofer IDMT, Oxford University, University of Passau, Umeå University, Zaizi, InsideOut10 Duration: 36 Months (end: 10/2016) Goal of MICO is to develop a platform to analyse “media in context” … ...by orchestrating different content extraction tools that can work simultaneously or in a sequence and ….. ...provide valuable metadata to third party applications.
3.
© Fraunhofer IDMT
3 Cross Media Recommendations in MICO The Recommendation Problem “Providing suggestions for items to be of use to a user” Broad Application domains: e-Commerce, Travel, Multimedia, News, … Two main data sources: User behaviour data (Collaborative Filtering) Item Metadata (Content based recommendation)
4.
© Fraunhofer IDMT
4 Cross Media Recommendations in MICO Media Recommendation “Classic” Recommendation problems (Music-Recommendation, Netflix Challenge) Mostly based on collaborative filtering, but also content analysis Cross Media Recommendation Cross Media ≠ Cross Modal Multiple Input Media Items + Associated Media context, e.g., commentary Multiple Output Not necessarily more of the same thing
5.
© Fraunhofer IDMT
5 Editor Support Usecase As an editor, while I create or edit articles using WordPress, I want to automatically get related articles and videos that I might link to the article. Project Use Case: Recommendations for Greenpeace Magazine Italia Articles Videos User behaviour data
6.
© Fraunhofer IDMT
6 Editor Support Usecase As an editor, while I create or edit articles using WordPress, I want to automatically get related articles and videos that I might link to the article. How can Mico Help? Articles: NER components for content spotting Videos Text to speech + NER Metadata Analysis User behaviour data Generic collaborative filtering component (prediction.io)
7.
© Fraunhofer IDMT
7 Chat Analysis Usecase As a zooniverse user, I want get more information that helps me during the current classification task, e.g., by the recommendation of similar subjects or training data. Input: Results from animal detection Sentiment and competence classification of talks NER for animal spotting Output: Classification Hints Hints for moderators / experts
8.
© Fraunhofer IDMT
8 Processing Workflow for Editor Support Use Case 1. Crawler feeds media items to platform e.g., videos from Greenpeace channel, news articles, internal archive…. 2. Analysis results are stored as Linked Data Currently: using Apache Marmotta as triplestore
9.
© Fraunhofer IDMT
9 Processing Workflow for Editor Support Use Case 3. Item gets into Focus (someone is writing an article on a specific topic) 4. Focused item is fed to the same platform, using customized pipelines 5. Results are processed by LD matching, i.e Recommendation component.. 6. … enriched by stored annotations … 7. And returned to editing platform
10.
© Fraunhofer IDMT
10 Recommendation Workflow in MICO
11.
© Fraunhofer IDMT
11 Ontology Matching Semantic distance of entities important for analysis Animal detection: “I see a bird on the tree”, “Yes it’s definitely a sparrow” Topic detection: Wind energy, sustainability, renewables, oil, … Non-Mico: Genres! (e.g., currently 1400 genres on Spotify…) Not less controversial: movie genres
12.
© Fraunhofer IDMT
12 Evaluation How to evaluate a recommender system? Forget about precision and recall! Observing user behaviour Online experiment Offline: extrapolating past user behavior Lab Studies / Questionnaire Open problem: standardized + comparable testing of recommender systems
13.
© Fraunhofer IDMT
13 Conclusions Proposed setting tries to formalize a certain subset of recommendation problems: Cross media recommendation Focused on Media Items Profits from Content Analysis New items, have a high contextual dependency on existing items, that will be exploited for recommendation Work in Progress: Stable release of Mico-platform and extractors: right now PoC recommendation code will be integrated in the platform Demo Services: available in June Incorporate feedback into recommendation
14.
© Fraunhofer IDMT T.
Köllmer†, E. Berndl‡, T. Weißgerber‡, P. Aichroth† , H. Kosch‡ A Workflow for Cross Media Recommendations based on Linked Data Analysis † Fraunhofer IDMT ‡ University of Passau Thank you! Questions?
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