Attention-Streams Recommendations

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
Recommender System:
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.

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
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 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 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 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:

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.

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Attention-Streams Recommendations

by Gregoire Burel on May 31, 2010

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