Attention-Streams Recommendations
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Real-time and Contextual Recommendations with Attention Modelling.

Real-time and Contextual Recommendations with Attention Modelling.

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Attention-Streams Recommendations Attention-Streams Recommendations Presentation Transcript

  • Attention-Streams
    GrégoireBurel, OAK Group, University Of Sheffield
    ESWC 2010, Heraklion,
    30 May 2010
  • Introduction
    Attention-Streams
    Attention-streams Recommendations:
    Contextual and real-time recommendations.
    Passive recommendations.
    Modelling Attention streams :
    Attention streams and existing recommendations.
    Attention vs. Interests.
    Modelling attention.
    Monitoring Attention.
    Attention based recommendations
    Demo:
    Video
    Conclusions
  • Recommender Systems
    Contextualizing information and users using cross-domain attention modeling.
    Recommender System:
    Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user.
    Information + Users + Interests
  • Recommender Systems
    Contextualizing information and users using cross-domain attention modeling.
    Recommender System:
    Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user.
    Information + Users + Interests
    Attention-Streams
    Attention-Streams
    (Real-time and Contextual Recommendations)
  • Attention-Streams RecommendationsContextual and Real-time Recommendations
  • Contextual and Real-time Recommendations
    Features:
    Models users interests across networks and communities:
    Interests are not fragmented.
    Recommendations matches real-time user interests:
    Information and user interests evolve rapidly independently of the users common interests.
    Real-time interests might be linked to FOAF profiles:
    Real-time interests can be shared between different contexts and application.
    Contextual ‘bookmarks’:
    Relevant recommendations might be bookmarked by the user.
    Content Recommendations:
    Local events using user location and current interests.
    Information sources using contextual RSS subscriptions.
    Real-time information streams given current interests.
  • Contextual and Real-time Recommendations
    Features:
    Models users interests across networks and communities:
    Interests are not fragmented.
    Recommendations matches real-time user interests:
    Information and user interests evolve rapidly independently of the users common interests.
    Real-time interests might be linked to FOAF profiles:
    Real-time interests can be shared between different contexts and application.
    Contextual ‘bookmarks’:
    Relevant recommendations might be bookmarked by the user.
    Content Recommendations:
    Local events using user location and current interests.
    Information sources using contextual RSS subscriptions.
    Real-time information streams given current interests.
  • Passive Recommendations
    Desktop
    Cross-domain
    Interests
    Mobile
    Local events + Information Streams + Contextual RSS
  • Passive Recommendations
    Recommendations do not require any particular action to be accessed:
    Users might ignore or access the recommendations without disturbing their current workflow.
  • Modelling Attention-StreamsAttention-Streams and Existing Recommendations
  • Attention-Streams and Existing Recommendations
    Contextualizing information and users using cross-domain attention modeling.
    Existing recommendations are fragmented, network specific, community dependent and long-term oriented (Resnick, 1997)
  • Attention-Streams and Existing Recommendations
    Movies
    Content
    Events
    Products
    Music
    People
  • Attention vs. Interests
    Modelling particular user Interests within a system or generic interests (Resnick, 1997).
    Explicit:
    “Tell me what you like”
    Implicit:
    “Let me guess what you like given what you do”.
    • Modelling information access and usage across domains.
    • User Activity: (Dragunov, 2005)
    • Work/Leisure.
    • News browsing, Finding a Restaurant…
    Long-term Interests
    Contextual ‘Interests’
    (Middleton, 2004)
  • Attention vs. Interests
    Attention Management:
    Attention models have been designed for dealing with interruption overload (attention management):
    Attention for information notification (Vertegaal, 2003) (Horvitz et al., 2003).
    Attention and Information Contextualisation:
    Attention is currently applied to information presentation.
  • Attention vs. Interests
    Attention Management:
    Attention models have been designed for dealing with interruption overload (attention management):
    Attention for information notification (Vertegaal, 2003) (Horvitz et al., 2003).
    Attention and Information Contextualisation:
    Attention is currently applied to information presentation.
    Attention-Streams
  • Attention vs. Interests
    Attention models can be used for recommending information:
    Attention  Interests / Interests  Attention
    Cross-domain Recommendations:
    Attention is community independent.
    Real-time recommendations:
    Attention is real-time / Interests are not (e.g. Middleton, 2004).
    Ambient Recommendations:
    Integration of the recommendations in the user workflow.
    Passive application.
    Recommender System:
    Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user.
  • Modelling Attention using Attention-Streams
    Attention Tag:
    AT = {agent, timestamp, domain, tag, weight (…)}
    Attention:
    AT = {agent, timestamp, AT set (…)}
    Attention Tags
    Attention
  • Attention Tag
    Attention is represented using lightweight semantics and weighted tags (APML Ontology).
    Each web document has corresponding attention tags.
    Attention-Tags might be linked to FOAF profiles.
    curio: Document
    curio: Agent
  • Attention
    AJAX
    politics
    word wide web
    At a specific instant, the attention of an Agent is characterized by a set of Attention Tags.
    Attention exists across domains.
    computing
    Model:
    • Attention-Tag Similarity:
    • WordNet, PMI, NSS (NGD (Cilibrasi, 2004))...
    • Attention-Range Affinity.
    • Attention-Range Calculation:
    • Affinity-Gradient, EMA…
  • Monitoring Attention
    Media Extraction Service
    WKI
    External Website
    Attention Tags
    External Website
    External Website
    WKI
    WKI
    External Website
    WKI
    External Website
    WKI
    External Website
    WKI
  • Attention Based Recommendations
    Media Extraction Service
    WKI
    External Website
    Attention Tags
    External Website
    External Website
    WKI
    WKI
    External Website
    WKI
    External Website
    WKI
    External Website
    WKI
  • Demohttp://nebula.dcs.shef.ac.uk/sparks/astreams
  • Conclusions
    Attention-Streams Recommendations:
    Contextual and Real-time information recommendations.
    Real-time interests modelling and sharing.
    Interests derived from user attention.
    Ambient recommendations.
  • Conclusions
    Attention-Streams Recommendations:
    Contextual and Real-time information recommendations.
    Real-time interests modelling and sharing.
    Interests derived from user attention.
    Ambient recommendations.
    Future work:
    More recommendations ! (i.e: Social).
    Integration with streaming ontologies and models (i.e: Sensor Streams).
    More Attention bookmarking.