A Federated Search and Social Recommendation Widget

A FEDERATED SEARCH AND SOCIAL RECOMMENDATION

WIDGETSten Govaerts, Sandy El Helou, Erik Duval and Denis Gillet.

Saturday 19 March 2011

WHAT’S A WIDGET ?!?

CONTEXT

• Personal Learning Environment:

• customizable• re-use, creation & mashup of tools, resources

• enable users to access content

• in different contexts

CONTEXT

ARCHITECTURE

DEMO...

PAGE RANKHYPERLINK

WEBSITE

A node is important if and only if many other important nodes point to it

Rank of node i:

friend

connection

saved/shared

disliked

friend

connection

saved/shared

OUR CASE

NOW FOR MULTI-DIRECTIONAL, PERSONALIZED & CONTEXTUAL RANKING

A node is important to a particular set of nodes (representing the target user and the context) if and only if many important nodes connected to this root set, via important relation types, point to it

EVALUATION

• 15 PhD students at K.U. Leuven and EPFL.

• What?

• usability

• user satisfaction

• usefulness

FIRST PHASE

• current media search tool: Google & YouTube

• understanding recommendations: 6/15 from like/dislike

FIRST PHASE

SECOND PHASE

• only 14 participants (one less)

• open questions

• usefulness of recommendations: 11/14 pro.

• user satisfaction: System Usability Scale (SUS) & MS Desirability Toolkit

• SUS score: 66,25%

• 2 groups

SECOND PHASE

• only 14 participants (one less)

• open questions

• usefulness of recommendations: 11/14 pro.

• user satisfaction: System Usability Scale (SUS) & MS Desirability Toolkit

• SUS score: 66,25%

• 2 groups K.U. Leuven: high (75%)EPFL + one K.U.Leuven: low (50%)

WHY THE DIFFERENT SUS?

• 1st phase by 2 interviewers

• issues:

• distracts of unrelated widget’s UI updates.

• layout too dense

• height of widgets too small

• KULeuven student had prior experience with iGoogle.

• not evaluating the widget but the whole experience...

DESIRABILITY...

RECOMMENDATIONS EVALUATION

• compare recommendations to their favourite tool: Google

• 2 groups with different queries

Precision# relevant items returned

# total items returned=

in top N list# relevant items returned

Precision at N

•Google: more relevant results

•Google: avg. prec. at 10 = 65%

• widget: avg. prec. at 10 = 50%

Precision at N

•Google: more relevant results

•Google: avg. prec. at 10 = 65%

• widget: avg. prec. at 10 = 50%

Precision at N

less variation in results

FIXES...

FUTURE WORK

• evaluation in larger scale real-world situation (university + business)

• evaluate user satisfaction of widget and not container

• evaluate the recommendations further (based on use).

•make recommendations transparent

MORE VISUAL SEARCH...

THANK YOU!QUESTIONS?...

A Federated Search and Social Recommendation Widget

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