Lorraine McGinty

Case-based recommender systems

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

On the role of diversity in conversational recommender systems

In the past conversational recommender systems have adopted a similarity-based approach to recommendation, preferring cases that are similar to some user query or profile. Recent research, however, has indicated the importance of diversity as an additional selection constraint. In this paper we attempt to clarify the role of diversity in conversational recommender systems, highlighting the pitfalls of naively incorporating current diversity-enhancing techniques into existing recommender systems. Moreover, we describe and fully evaluate a powerful new diversity-enhancing technique that has the ability to significantly improve the performance of conversational recommender systems across the board.

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the users feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a features value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a users critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.

Comparison-Based Recommendation

Recommender systems combine user profiling and filtering techniques to provide more pro-active and personal information retrieval systems, and have been gaining in popularity as a way of overcoming the ubiquitous information overload problem. Many recommender systems operate as interactive systems that seek feedback from the end-user as part of the recommendation process to revise the users query. In this paper we examine different forms of feedback that have been used in the past and focus on a low-cost preference-based feedback model, which to date has been very much under utilised. In particular we describe and evaluate a novel comparison-based recommendation framework which is designed to utilise preference-based feedback. Specifically, we present results that highlight the benefits of a number of new query revision strategies and evidence to suggest that the popular more-like-this strategy may be flawed.

Group recommender systems: a critiquing based approach

Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.

Experiments in dynamic critiquing

Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.

Distributed case-based reasoning

Distribution of resources within case-based reasoning (CBR) architectures is beneficial in a variety of application contexts. This article briefly discusses some of the approaches that fall under the heading of distributed CBR, and their general impact.

Collaborative Case-Based Reasoning: Applications in Personalised Route Planning

Distributed case-based reasoning architectures have the potential to improve the overall performance of case-based reasoning systems. In this paper we describe a collaborative case-based reasoning architecture, which allows problem solving experiences to be shared among multiple agents. We demonstrate how this technique can be used successfully to solve an important challenge in the area of personalised route planning; the problem of how to generate route plans that conform to a users implicit travel preferences in an unfamiliar map territory.

On the Evolution of Critiquing Recommenders

Over the past decade a significant amount of recommender systems research has demonstrated the benefits of conversational architectures that employ critique-based interfacing (e.g., Show me more like item A, but cheaper). The critiquing phenomenon has attracted great interest in line with the growing need for more sophisticated decision/recommendation support systems to assist online users who are overwhelmed by multiple product alternatives. Originally proposed as a powerful yet practical solution to the preference elicitation problem central to many conversational recommenders, critiquing has proved to be a popular topic in a variety of related areas (e.g., group recommendation, mixed-initiative recommendation, adaptive user interfacing, recommendation explanation). This chapter aims to provide a comprehensive, yet concise, source of reference for researchers and practitioners starting out in this area. Specifically, we present a deliberately non-technical overview of the critiquing research which has been covered in recent years.

Adaptive Selection: An Analysis of Critiquing and Preference-Based Feedback in Conversational Recommender Systems

E-commerce recommender systems help consumers to locate products within a complex product-space. Conversational recommender systems engage the user in a multicycle session, suggesting one or more products during each cycle, and using the feedback to inform the suggestions for the next cycle. By combining user feedback over several cycles, the system obtains a clear picture of the product the user wishes to purchase. As demonstrated under several experimental conditions, the performance of recommender systems is dramatically improved by the technique of adaptive selection, which employs critiquing and preference-based feedback, and emphasizes product diversity rather than similarity as a selection constraint.