| Authors: |
Lorcan Coyle
|
| URL: |
https://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-32.pdf |
| Description: |
Lorcan Coyle's Bibliography |
| Tags: |
cbr
context
machine_learning
personalisation
recommender_systems
|
| Abstract: |
As e-commerce has become more popular, the problem of information
overload has come to the fore. Recommender systems that reduce the
information overload problem are becoming more common. However, the
problem with many recommender systems is that they are associated
with a high cost of learning customer preferences (in terms of cognitive
load). We describe the Personal Travel Assistant (PTA), a flight
recommender application that uses case-based reasoning (CBR) to overcome
these problems.
The PTA allows users to search multiple flights providers concurrently
and recommends flights based on their individual travel preferences.
These preferences are implicitly learned from observations of user
behaviour. When the user purchases a flight, the PTA uses the selection
of a preferred flight to discover and refine the user’s overall travel
preferences. These preferences are stored in a user-model as sets
of cases representing their interactions, which are used to provide
personalised recommendations.
The PTA makes recommendations taking into account the context in which
the flights were offered. It uses features from the request to determine
this context, e.g. the duration of the trip. We perform evaluations
of contextual recommendations that support our view that user preferences
change depending on the context of the session. We further improve
recommendation accuracy by storing and personalising similarity measures
in the user-model. The PTA alters the relative importance of features
in the personal similarity measure based on implicit user feedback,
e.g. increasing the importance of price at the cost of stop-over
time in a multiple hop flight.
We also investigate cooperative components to extend our recommendation
strategies. These allow users to reuse the information learned from
other users when they encounter new situations. However, these techniques
are not as successful as we had hoped. We discuss these components
in relation to other work on collaborative recommendation and suggest
that the standard approach is unsuited to the PTA’s context-based
recommendation strategy.
The strength of CBR in the e-commerce domain stems from its reuse
of the knowledge base associated with a particular application. Since
case data may be one aspect of a company’s entire knowledge system,
it is important to integrate case data easily within a company’s
IT infrastructure, providing in effect a case-based view on relevant
portions of the company knowledge base. We describe CBML, an XML-based
Case Mark-Up Language we have developed to facilitate such integration. |
@phdthesis{Coyle2004Making,
title = {Making Personalised Flight Recommendations using Implicit Feedback},
author = {Lorcan Coyle},
school = {Trinity College Dublin},
url = {https://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-32.pdf},
year = {2004},
description = {Lorcan Coyle's Bibliography},
abstract = {As e-commerce has become more popular, the problem of information
overload has come to the fore. Recommender systems that reduce the
information overload problem are becoming more common. However, the
problem with many recommender systems is that they are associated
with a high cost of learning customer preferences (in terms of cognitive
load). We describe the Personal Travel Assistant (PTA), a flight
recommender application that uses case-based reasoning (CBR) to overcome
these problems.
The PTA allows users to search multiple flights providers concurrently
and recommends flights based on their individual travel preferences.
These preferences are implicitly learned from observations of user
behaviour. When the user purchases a flight, the PTA uses the selection
of a preferred flight to discover and refine the user’s overall travel
preferences. These preferences are stored in a user-model as sets
of cases representing their interactions, which are used to provide
personalised recommendations.
The PTA makes recommendations taking into account the context in which
the flights were offered. It uses features from the request to determine
this context, e.g. the duration of the trip. We perform evaluations
of contextual recommendations that support our view that user preferences
change depending on the context of the session. We further improve
recommendation accuracy by storing and personalising similarity measures
in the user-model. The PTA alters the relative importance of features
in the personal similarity measure based on implicit user feedback,
e.g. increasing the importance of price at the cost of stop-over
time in a multiple hop flight.
We also investigate cooperative components to extend our recommendation
strategies. These allow users to reuse the information learned from
other users when they encounter new situations. However, these techniques
are not as successful as we had hoped. We discuss these components
in relation to other work on collaborative recommendation and suggest
that the standard approach is unsuited to the PTA’s context-based
recommendation strategy.
The strength of CBR in the e-commerce domain stems from its reuse
of the knowledge base associated with a particular application. Since
case data may be one aspect of a company’s entire knowledge system,
it is important to integrate case data easily within a company’s
IT infrastructure, providing in effect a case-based view on relevant
portions of the company knowledge base. We describe CBML, an XML-based
Case Mark-Up Language we have developed to facilitate such integration.},
keywords = {cbr context machine_learning personalisation recommender_systems }
}
::