B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway: a recommendation computation approach based on user behaviour

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999

Broadway: a recommendation computation approach based on user behaviour similarity

B. Trousse & R. Kanawati

Action AID, INRIA Sophia-Antipolishttp://www.inria.fr/aid


Planning

The Broadway approach

BeCBKB: query refinement advisor

Recommendation systems

Applications :

Broadway-V1 : web browsing advisor


Recommendation systems

Raw Recommendati

on data

producers

Recommendation

consumers

Raw recommendations

data

Raw recommendation data

collecting

Recommendation

Computing

Recommendation computation module

Computed recommendations


Web sites recommendation approaches

Profile-Based approaches

•Content-based recommendation

•Collaboratif filtering

Data-mining based approaches

•Web-usage data analysis

Access data

Users behaviour


The Broadway approach

Recommend to a user what others that have behaved similarly had positively evaluated

Using case based reasoning

Variable observation based behaviour modelling

Principle:

Features:


Broadway implementation

Implementation: CBR*Tools a frameworkfor CBR applications applied on cases with time

extendedsituations.

Implementation: CBR*Tools a frameworkfor CBR applications applied on cases with time

extendedsituations.

• Modelling the user behaviour

• Determining the set of variables to observe

• Define the case structure: problem and Solution.

• Define behaviours similarity measurements

• Retrieving past useful experiences

• Evaluating and adapting found solutions

• Modelling the user behaviour

• Determining the set of variables to observe

• Define the case structure: problem and Solution.

• Define behaviours similarity measurements

• Retrieving past useful experiences

• Evaluating and adapting found solutions


Broadway cycle

Case base+

Raw observations+

Field knowledge

Session

RetrievalRetrieval

ReuseReuse

ReviseRevise

RetainRetain

Target case = (Current behaviour, ?)

Target case+

Retrieved cases

Recommendations

Source case=(behaviour,recommended actions)

Target case=(Behaviour, adapted actions)

Target case=(Behaviour, revised recommendedactions)


Applying the Broadway approach

Browsing the web : Broadway-V1

Query refinement advisor : Broadway-QR

Web site browsing helper.


Broadway-V1 : user interface


Broadway-V1 : Distributed architecture

Event Dispatcher

Page Info Server

Recommendation Server

Proxy

R e a d /w r ite a n n o ta t io n s

R e a d a n n o ta t io n s

p a g e

p a g e

P a g e d e s c r ip tio n

P a g e lo a d in gP a g e e r ro r

R e c o m m e n d a t io n sE v e n ts

Serve r S ideC lien t S ide

User Info ServerP ro f ile s

S ta r t /S to p , E v a lu a tio n sP a g e d is p la y d u ra t io n

Browser, toolbars and manager

W W W

Annotation Server


Page content

Page address

Display timeratio

Navigation

Evaluation

#7 #8 #9 #5 #13

Case = Time-extended situation + List of relevant pages

Relevantpage

#3

before

Time Reference

Context Restriction

Broadway-V1: case structure


• Procedure– 2 groups with a well defined information searching goal– Initialisation: 20 navigations

• Results With Broadway

Without– Number of success 3/4 2/6– Avg. duration 18 min 24

min– Avg. length of navigations 19 p. 39 p.

Broadway-V1 : experimental evaluation


Broadway-V1: current situation & future work

Validation of the prediction feature of the Broadway approach for supporting browsing

Study of a new version of Broadway V1 as a multi-agent system

Methodological support for the configuration of such Broadway-based recommender systems in specific application classes


Broadway-QR: A QR recommender

* In collaboration with XRCE

IR server

Ex. CBKB, WSQLIR server wrapper

Event server

QR-Recommender

IR clientIR client

Broadway-QRBroadway-QR


Example : The BeCBKB* System


User behaviour modelling

Reference instant

Case = Behavioural situation + Recommended queries

Selected Doc (Key words)

Query keywords

Query Results

Doc. Evaluation

Context: Session summery

Query Eval

RestrictionSolution


Recommendation computation

Construct the target caseApply the case template on the current session at the current instant.

Retrieve1. Find past sessions with the most similar context.

2. Find cases with the most similar restriction.

A set of cases

3. Find cases with the most similar elementarybehaviour. If no cases are returned then restartfrom from step 2 on the set of potential casesthat could be extracted form sessions determined in step 1.

A Subset of cases

Reuse Rank solutions returned by the previous stepby using some utility function.Ex. distance form the current query configuration


* University of Torento** University of Glasgow

A validation study on query test data bases** is planned.

Broadway-QR : current situation and future work

A WebSQL* Wrapper is implemented

A CBKB Wrapper is under implementation

Broadway-QR as an optimiser of other query refinement schemes

Documents

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway: a recommendation computation approach based on user behaviour