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Broadway: a recommendation computation approach based on user behaviour similarity

Broadway: a recommendation computation approach based on user behaviour similarity. B. Trousse & R. Kanawati. Action AID, INRIA Sophia-Antipolis http://www.inria.fr/aid. Planning. Recommendation systems. The Broadway approach. Applications :. Broadway-V1 : web browsing advisor.

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Broadway: a recommendation computation approach based on user behaviour similarity

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  1. Broadway: a recommendation computation approach based on user behaviour similarity B. Trousse & R. Kanawati Action AID, INRIA Sophia-Antipolis http://www.inria.fr/aid

  2. Planning Recommendation systems The Broadway approach Applications : Broadway-V1 : web browsing advisor BeCBKB: query refinement advisor

  3. Recommendation systems Raw recommendations data Recommendation computation module Raw Recommendation data producers Raw recommendation data collecting Recommendation consumers Recommendation Computing Computed recommendations

  4. Web sites recommendation approaches Profile-Based approaches • Content-based recommendation • Collaboratif filtering Data-mining based approaches • Web-usage data analysis Access data Users behaviour

  5. The Broadway approach Principle: Recommend to a user what others that have behaved similarly had positively evaluated Features: Using case based reasoning Variable observation based behaviour modelling

  6. Broadway implementation • 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 Implementation: CBR*Tools a framework for CBR applications applied on cases with time extended situations.

  7. Broadway cycle Session Target case = (Current behaviour, ?) Retrieval Target case + Retrieved cases Source case= (behaviour,recommended actions) Case base + Raw observations + Field knowledge Retain Reuse Target case= (Behaviour, adapted actions) Target case= (Behaviour, revised recommended actions) Recommendations Revise

  8. Applying the Broadway approach Browsing the web : Broadway-V1 Query refinement advisor : Broadway-QR Web site browsing helper.

  9. Broadway-V1 : user interface

  10. Broadway-V1 : Distributed architecture

  11. Broadway-V1: case structure Case = Time-extended situation + List of relevant pages before Relevant page Context Restriction Page address #3 #5 #7 #8 #9 #13 Page content Evaluation Display time ratio Time Reference Navigation

  12. Broadway-V1 : experimental evaluation • 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.

  13. Broadway-V1: current situation & future work Study of a new version of Broadway V1 as a multi-agent system Validation of the prediction feature of the Broadway approach for supporting browsing Methodological support for the configuration of such Broadway-based recommender systems in specific application classes

  14. Broadway-QR: A QR recommender Event server IR server IR client IR server wrapper Ex. CBKB, WSQL QR-Recommender Broadway-QR * In collaboration with XRCE

  15. Example : The BeCBKB* System

  16. Case = Behavioural situation + Recommended queries User behaviour modelling Solution Context: Session summery Restriction Query keywords Selected Doc (Key words) Doc. Evaluation Query Results Query Eval Reference instant

  17. Construct the target case Retrieve A set of cases 2. Find cases with the most similar restriction. A Subset 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. Reuse Recommendation computation Apply the case template on the current session at the current instant. 1. Find past sessions with the most similar context. Rank solutions returned by the previous step by using some utility function. Ex. distance form the current query configuration

  18. Broadway-QR : current situation and future work A WebSQL* Wrapper is implemented A CBKB Wrapper is under implementation A validation study on query test data bases** is planned. Broadway-QR as an optimiser of other query refinement schemes * University of Torento ** University of Glasgow

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