Attention-Streams

Attention-Streams

Grégoire Burel, OAK Group, University Of Sheffield

ESWC 2010, Heraklion,30 May 2010

Attention-Streams

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

Attention-Streams

Recommender Systems

• Contextualizing information and users using cross-domain attention modeling.

Recommender System:Type of information filtering system technique that attempts to recommend information items that are likely to be of interest to the user.

Recommender System:Type of information filtering system technique that attempts to recommend information items that are likely to be of interest to the user.

Information + Users + Interests

Attention-Streams

Recommender Systems

• Contextualizing information and users using cross-domain attention modeling.

Recommender System:Type of information filtering system technique that attempts to recommend information items that are likely to be of interest to the user.

Recommender System:Type of information filtering system 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

Attention-Streams RecommendationsContextual and Real-time Recommendations

Attention-Streams

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.

•–

–

–

Attention-Streams

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.

Attention-Streams

Passive Recommendations

Local events + Information Streams + Contextual RSS

Mobile

DesktopCross-domain

Interests

Attention-Streams

Passive Recommendations

• Recommendations do not require any particular action to be accessed:– Users might ignore or access the recommendations without disturbing their current

workflow.

Attention-Streams

Modelling Attention-StreamsAttention-Streams and Existing Recommendations

Attention-Streams

Attention-Streams and Existing Recommendations

• Contextualizing information and users using cross-domain attention modeling.

Recommender System:Type of information filtering system technique that attempts to recommend information items that are likely to be of interest to the user.

Existing recommendations are fragmented, network specific, community dependent and long-term oriented (Resnick, 1997)

Attention-Streams

Attention-Streams and Existing Recommendations

Movies

Events

Music

Content

People

Products

Attention-Streams

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”.

User Interests

• Modelling information access and usage across domains. – User Activity: (Dragunov, 2005)

• Work/Leisure.• News browsing, Finding a

Restaurant…

Attention Profiling

Contextual ‘Interests’Long-term Interests

(Middleton, 2004)

Attention-Streams

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:The cognitive process of selectively concentrating on one aspect of the environment while ignoring other things.

Attention-Streams

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:The cognitive process of selectively concentrating on one aspect of the environment while ignoring other things.

Attention-Streams

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 filtering system technique that attempts to recommend information items that are likely to be of interest to the user.

Attention-Streams

Modelling Attention using Attention-Streams

– Attention Tag:• AT = {agent, timestamp,

domain, tag, weight (…)}

– Attention:• AT = {agent, timestamp,

AT set (…)}

Attention-Stream:The continuous flow of interrelated information accessed through the Attention of an Agent.

Attention

Attention Tags

Attention-Streams

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-Streams

Attention

• At a specific instant, the attention of an Agent is characterized by a set of Attention Tags.– Attention exists across domains.

Model:• Attention-Tag Similarity:

— WordNet, PMI, NSS (NGD (Cilibrasi, 2004))...

• Attention-Range Affinity.• Attention-Range Calculation:

— Affinity-Gradient, EMA…

politics word wide web AJAX

computing

Attention-Streams

Monitoring AttentionMedia Extraction Service

Attention-Streams

Attention Based RecommendationsMedia Extraction Service

Attention-Streams

Demohttp://nebula.dcs.shef.ac.uk/sparks/astreams

Attention-Streams

Conclusions

• Attention-Streams Recommendations:– Contextual and Real-time information recommendations.– Real-time interests modelling and sharing.

• Interests derived from user attention.– Ambient recommendations.

Full Live Demo:Tomorrow, during the poster and demo session.

Attention-Streams

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.

Full Live Demo:Tomorrow, during the poster and demo session.