feature weight learning

Making Personalised Flight Recommendations using Implicit Feedback

Publication Type:

Thesis

Authors:

Lorcan Coyle

Source:

Computer Science Department, Trinity College Dublin, Dublin (2004)

Keywords:

case based reasoning; cbml; personal travel assistant; xml; feature weight learning; recommender systems; context awareness

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.

Exploiting Re-ranking Information in a Case-Based Personal Travel Assistant

Publication Type:

Conference Paper

Source:

Workshop on Mixed-Initiative Case-Based Reasoning at the Fifth International Conference on Case-Based Reasoning, Trondheim, Norway, p.11-20 (2003)

Keywords:

case based reasoning; personal travel assistant; feature weight learning

Abstract:

Intelligent software assistants are becoming more common in the e-commerce domain. We are working on a personal travel assistant. The goal of this application is to use case based reasoning to assist the user in arranging flights. It offers personalised service to its users and automatically learns their travel preferences. It stores these preferences in a user model that is directly related to the CBR process. It learns the user preferences by exploiting user feedback on sets of presented travel offers. When the user selects a preferred offer, the PTA establishes a preference ordering among the whole set. This ordering is calculated by measuring the similarity between the selected offer and each of the other offers. This ordering is used to rate these offers and store them in the user profile as cases. This ordering is also used to refine the user's overall travel preferences by altering their personal similarity measure.

Improving Recommendation Ranking by Learning Personal Feature Weights

Publication Type:

Conference Paper

Source:

Advances in Case-Based Reasoning, 7th European Conference, ECCBR 2004, Springer, Madrid, Spain, p.560-572 (2004)

ISBN:

978-3-540-22882-0

Keywords:

case based reasoning; personal travel assistant; feature weight learning

Abstract:

The ranking of offers is an issue in e-commerce that has received a lot of attention in Case-Based Reasoning research. In the absence of a sales assistant, it is important to provide a facility that will bring suitable products and services to the attention of the customer. In this paper we present such a facility that is part of a Personal Travel Assistant (PTA) for booking flights online. The PTA returns a large number of offers (24 on average) and it is important to rank them to bring the most suitable to the fore. This ranking is done based on similarity to previously accepted offers. It is a characteristic of this domain that the case-base of accepted offers will be small, so the learning of appropriate feature weights is a particular challenge. We describe a process for learning personalised feature weights and present an evaluation that shows its effectiveness

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