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Requirements for Processing Datasets for Recommender Systems
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Requirements for Processing Datasets for Recommender Systems


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  • Example of using Recommendation API: recommend(itemURI,limit_of_resources), recommend(itemURI,user_tags) Example of social data API provided by the aggregator: get_tags(itemURI), get_reviews(itemURI) etc
  • Here we present the architecture of such an environment and the proposed software stackMonte Carlo will be a separate component that can run also on the Grid and that will br provided through an API. The API will be documented.
  • Transcript

    • 1. Requirements for Processing Datasets for Recommender Systems Preliminary Experiences from Three Case Studies Giannis Stoitsis University of Alcala, Spain Agro-Know Technologies, Greece RecSys Challenge 2012, Dublin
    • 2. the learning case• technology-enhanced learning investigates how information and communication technologies can be used to support learning and teaching, and competence development throughout life.• various levels/contexts – school – higher education and research – vocational education and training – adult education
    • 3. recommend resources in moodle
    • 4. recommend resources in learning portal
    • 5. handling multiple, diverse sets & streams• various types of social data• different schemas and formats• multiple languages and dimensions Single criteria Multi-criteria
    • 6. why?• support various usage and recommendation scenarios• combining data from various sources may boost the way recommender work in education – bigger data – federated recommender systems – open science platform
    • 7. a European social data infrastructure for learning …portals… Meta Social Meta Social Meta Social Social data data Data Data data Data Data API API API API Federated Aggregation of metadata, social and usage dataRecommendation services Resolution services Social Metadata Data per URI Anonymised
    • 8. challenges• define common metadata schema• harvest/crawl social data• transform each social data schema• uri resolution• scalability• anonymised approach• develop item-based non personalized algorithms that can perform well
    • 9. our open science case study
    • 10. web app for testing neighborhood-based recommendation algorithms with multi-criteria rating dataset Export data (sql, csv) I need Refine more!!! Login data Transfom Import dataset dataset (sql, csv, xml) Create Prepare dataset dataset Data characteristics Visualize dataset Visualize RecSys Export results researcher/ results developer
    • 11. architecture Web UI Developers APIComponents Refine and Prepare/p Import Visualize Evaluate transform rocess APICloud/Grid infra Monte Carlo Social Social Social Recommender Data Data Data Simulator services
    • 12. experience from Mendeley case
    • 13. experience from multi-criteria rating dataset from a teachers portal e.g. integration in classroom, relevance to topics, ability to help students learn Size of the neighborhood Correlation Weight Threshold value
    • 14. DEMO