Paper Info
Title
Improving Case Representation and Case Base Maintenance in Recommender Agents
Abstract
Recommendations bysalesp eople are always based on knowledge about the products and expertise about your tastes, preferences, interests and behavior in the shop. In an attempt to model the behavior of salespeople, AI research has been focussed on the so called recommender agents. Such agents draw on previous results from machine learning and other advances in AI technologyto develop user models and to anticipate and predict user preferences. In this paper we introduce a new approach to recommendation, based on Case-Based Reasoning (CBR). CBR is a paradigm for learning and reasoning through experience, as salesmen do. We present a user model based on cases in which we try to capture both explicit interests (the user is asked for information) and implicit interests (captured from user interaction) of a user on a given item. Retrieval is based on a similarityfunction that is constantlytuned according to the user model. Moreover, in order to cope with the utility problem that current CBR system suffer from, our approach includes a forgetting mechanism (the drift attribute) that can be extended to other applications beyond e-commerce.
Year
DOI
Venue
2002
10.1007/3-540-46119-1_18
ECCBR
Keywords
Field
DocType
user preference,implicit interest,user interaction,new approach,drift attribute,recommender agents,improving case representation,explicit interest,ai research,case-based reasoning,user model,current cbr system,case base maintenance,case base reasoning,machine learning,e commerce
Recommender system,Forgetting,Systems theory,Computer science,Case base,Human–computer interaction,Artificial intelligence,User modeling,Knowledge base,Case-based reasoning,Machine learning,Distributed computing
Conference
ISBN
Citations 
PageRank 
3-540-44109-3
9
0.96
References 
Authors
14
3
Name
Order
Citations
PageRank
Miquel Montaner120523.60
Beatriz López211413.76
Josep Lluís De La Rosa326041.38