An Adaptive System for User Information needs based on the observed meta- Knowledge AKERELE Olubunmi Doctorate student, University of Ibadan, Ibadan, Nigeria; DICEN-IDF laboratory, France OSOFISAN Adenike Professor,Department of Computer Science University of Ibadan, Ibadan, Nigeria

Plan  Introduction  Aim  Model for capturing activities of the user  Proposal for the adaptive system  Statistical reasoning  Conclusion 2

Introduction 3 User interacts with information retrieval system everyday, but having access to relevant document to their information needs has been the source of concern

Introduction……  Its being observed and proved that the better one can formulate his information needs the better the search result  Which means is not a question of information overload or overkill  Its all about the knowledge that the user has on his information needs 4

Aim  The aim of this work is to provide an efficient adaptive approach for information retrieval based on users’ past experiences 5

Two kinds of users David & Bueno 1999 identified two kinds of users  Casual and regular user  Casual user are users for which there is little or no information concerns about their particularities  Regular users are users that use the system regularly for similarity. Their past experiences and the user’s choices on the past solutions are to be integrated while proposing new solution for these kinds of users 6

Provide adaptation effect Collect information on the regular user His identity His activities His preferences Adaptive System 7

Structure of model 8 Application { User { (Domain { Activity { (activity_id, Type of activity, Concept, Time)}})}}

CBR for inferring element of certainty 9  Represent the user’s activities  Keep record of the user’s activities  Keep record of the elements of knowledge employed by the user

Proposal  In order to provide an adaptive result, considering the user knowledge  Resolving an information retrieval problem from a user perspective in competitive intelligence system, we propose a model for an information retrieval system that consists of :  Set of document in the repository D = {d 1, d 2, d 3,.,.,., d n }  A subset R d of D containing relevant information that satisfy the user objective 10

Experience/Case 11

Experience  Experience = {objective, User, Activities, Concept, Time}  Experience = {O, U, A, C, T}  f : ({O,U.A,C,T}, D)  R d  Based on the above equation the document retrieved R d has put into consideration the experience of the user 12

 Objective: is the problem or the information need of the user  Activities: are ways of demonstrating his objective to the system and which will lead to the resolution of his information need  Concepts: are terms used in the process of performing any of the activities  Timestamp: is the time at which that particular activity takes place using that particular concept 13

 The concept used in performing an activity depends on whom the user is or his experience in that domain. A dependent relation can be established on paired users and concept to a decisional problem (objective) at a particular time 14

Graphical Representation of IR framework proposed 15

Statistical Reasoning  A possible approach for modelling the user of this kind of system is using statistical reasoning  Each element of P(D) possible set of information,has an associated value representing the probability that the set of information satisfies the objectives of the user  The probability value is updated as time passes on the basis of interaction between the user and the IR system.  The IR system uses all the knowledge gathered from the user and determine which element R d of set P(D) satisfies the user’s objectives 16

Dynamic Bayesian Network  It can be used to represent random variable and it also allows the state of the system to be represented as a hidden variable and observed states in terms of state variable among which there can be complex dependencies (Torres and Parkes, 2000), (Afolabi and Thiery, 2006)  For our proposal, given the observed user activities and using a prior graphical model, objective is to infer user’s need and ultimately to calculate R d – set of relevant information. User’s need / objective is a time dependent hidden variable that must be known in order to be able to compute R d 17

Conclusion  This work is an on-going research, not yet fully implemented  The model when fully implemented will assist users in obtaining information relevant to their needs 18

 Thank you 19