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-Antipolis

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Planning The Broadway approach BeCBKB: query refinement advisor Recommendation systems Applications : Broadway-V1 : web browsing advisor

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Recommendation systems Raw Recommendation data producers Recommendation consumers Raw recommendations data Raw recommendation data collecting Recommendation Computing Recommendation computation module Computed recommendations

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Web sites recommendation approaches Profile-Based approaches Content-based recommendation Collaboratif filtering Data-mining based approaches Web-usage data analysis Access data Users behaviour

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

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway implementation Implementation: CBR*Tools a framework for CBR applications applied on cases with time extended situations. Implementation: CBR*Tools a framework for CBR applications applied on cases with time extended situations. 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

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway cycle Case base + Raw observations + Field knowledge Session Retrieval Reuse Revise Retain Target case = (Current behaviour, ?) Target case + Retrieved cases Recommendations Source case= (behaviour,recommended actions) Target case= (Behaviour, adapted actions) Target case= (Behaviour, revised recommended actions)

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Applying the Broadway approach Browsing the web : Broadway-V1 Query refinement advisor : Broadway-QR Web site browsing helper.

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway-V1 : user interface

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway-V1 : Distributed architecture

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Page content Page address Display time ratio Navigation Evaluation #7 #8 #9 #5#13 Case = Time-extended situation + List of relevant pages Relevant page #3 before Time Reference Context Restriction Broadway-V1: case structure

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 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. duration18 min24 min –Avg. length of navigations 19 p.39 p. Broadway-V1 : experimental evaluation

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

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway-QR: A QR recommender * In collaboration with XRCE IR server Ex. CBKB, WSQL IR server wrapper Event server QR-Recommender IR client Broadway-QR

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Example : The BeCBKB* System

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

B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Recommendation computation Construct the target case Apply the case template on the current session at the current instant. Retrieve 1. 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 elementary behaviour. If no cases are returned then restart from from step 2 on the set of potential cases that could be extracted form sessions determined in step 1. A Subset of cases Reuse Rank solutions returned by the previous step by using some utility function. Ex. distance form the current query configuration

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