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Attention-Streams Recommendations

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Real-time and Contextual Recommendations with Attention Modelling.

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Attention-Streams Recommendations

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

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