Ecai 2008 Workshop on Recommender Systems (original) (raw)
Related papers
Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBR
Thirteenth UK Workshop on Case …, 2008
This paper describes the various explanation capabilities of the open source case-based reasoning tool myCBR. myCBR features conceptual explanations, which provide information about concepts of the application domain, backward explanations, which explain results of the retrieval process, and forward explanations, which support in the modelling of similarity measures. myCBR has been developed as a rapid prototyping tool with a general purpose interface as well as a similarity-based retrieval engine for easy integration in other applications where the explanations can be further adapted to the application's requirements.
A Case-Based Reasoning Approach to Collaborative Filtering
Lecture Notes in Computer Science, 2000
Collaborative filtering systems make recommendations based on the accumulation of ratings by many users. The process has a case-based reasoning flavor: recommendations are generated by looking at the behavior of other users who are considered similar. However, the features associated with a user are semantically weak compared with those used by CBR systems. This research examines multi-dimensional or semantic ratings in which a system gets information about the reason behind a preference. Experiments show that metrics in which the semantic meaning of each rating is taken into account have markedly superior performance than simpler techniques.
A Case-Based Reasoning View of Automated Collaborative Filtering
Case-Based Reasoning Research …, 2001
From some perspectives Automated Collaborative Filtering (ACF) appears quite similar to Case-Based Reasoning (CBR). It works on data organised around users and assets that might be considered case descriptions. In addition, in some versions of ACF, much of the induction is deferred to run time -in the lazy learning spirit of CBR. On the other hand, because of its lack of semantic descriptions it seems to be the antithesis of case-based reasoninga learning approach based on case representations. This paper analyses the characteristics shared by ACF and CBR, it highlights the differences between the two approaches and attempts to answer the question "When is it useful or valid to consider ACF as CBR?". We argue that a CBR perspective on ACF can only be useful if it offers insights into the ACF process and supports a transfer of techniques. In conclusion we present a case retrieval net model of ACF and show how it allows for enhancements to the basic ACF idea.
Explanations in Recommender Systems Overview and Research Approaches
2013
Recommender systems are software tools that supply users with suggestions for items to buy. However, it was found that many recommender systems functioned as black boxes and did not provide transparency or any information on how their internal parts work. Therefore, explanations were used to show why a specific recommendation was provided. The importance of explanations has been approved in a number of fields such as expert systems, decision support systems, intelligent tutoring systems and data explanation systems. It was found that not generating a suitable explanation might degrade the performance of recommender systems, their applicability and eventually their value for monetization. Our goal in this paper is to provide a comprehensive review on the main research fields of explanations in recommender systems along with suitable examples from literature. Open challenges in the field are also manifested. The results show that most of the work in the field focus on the set of characteristics that can be associated with explanations: transparency, validity, scrutability, trust, relevance, persuasiveness, comprehensibility, effectiveness, efficiency, satisfaction and education. All of these characteristics can increase the system's trustworthiness. Other research areas include explanation interfaces, over and underestimation and decision making
2018
Within the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system to facilitate transparent retrieval of task information for field engineers in the context of service delivery. The presented software adheres to the five goals of an explainable intelligent system and incorporates elements of both Case-Based Reasoning and heuristic techniques to develop a recommendation ranking of tasks. In addition we evaluate methods of building justifiable representations for similarity-based return on a classification task developed from engineers' notes. Our conclusion highlights the trade-off between p...
Case-based reasoning and legacy data reuse for web-based recommendation architectures
Proceedings of the Third International Conference on Information Integration and Web-based Applications & Services, 2001
Abstract: This paper describes a software framework for developing case-based reasoning (CBR) components that are seamlessly integrated into an enterprise architecture. The framework exploits recent standardization initiatives in the area of XML databases and mediator systems. We show how a legacy database or an XML data source, which may be tagged using a standard vocabulary, can be easily reused as case base with minimal additional efforts. An application of the proposed framework to the development of a case- ...
Case-based recommender systems: A unifying view
2005
This paper presents a unifying framework to model case-based reasoning recommender systems (CBR-RSs). CBR-RSs have complex architectures and specialize the CBR problem solving methodology in a number of ways. The goal of the proposed framework is to illustrate both the common features of the various CBR-RSs as well as the points were these systems take different solutions.
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce, 2006
The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.
Automated Product Recommendations with Preference-Based Explanations
Journal of Retailing, 2020
Many online retailers, such as Amazon, use automated product recommender systems to encourage customer loyalty and cross-sell products. Despite significant improvements to the predictive accuracy of contemporary recommender system algorithms, they remain prone to errors. Erroneous recommendations pose potential threats to online retailers in particular, because they diminish customers' trust in, acceptance of, satisfaction with, and loyalty to a recommender system. Explanations of the reasoning that lead to recommendations might mitigate these negative effects. That is, a recommendation algorithm ideally would provide both accurate recommendations and explanations of the reasoning for those recommendations. This article proposes a novel method to balance these concurrent objectives. The application of this method, using a combination of content-based and collaborative filtering, to two real-world data sets with more than 100 million product ratings reveals that the proposed method outperforms established recommender approaches in terms of predictive accuracy (more than five percent better than the Netflix Prize winner algorithm according to normalized root mean squared error) and its ability to provide actionable explanations, which is also an ethical requirement of artificial intelligence systems.
Text-based recommender system with explanatory capabilities
Recommender systems have proven their usefulness both for companies and customers. The former increase their sales and the latter get a more satisfying shopping experience. These systems can benefit from the advent of explainable artificial intelligence, since a well-explained recommendation will be more convincing and may broaden the customer’s purchasing options. Many approaches offer justifications for their recommendations based on the similarity (in some sense) between users, past purchases, etc., which require some knowledge of the users. In this paper we present a recommender system with explanatory capabilities which is able to deal with the so-called cold-start problem, since it does not require any previous knowledge of the user. Our method learns the relationship between the products and some relevant words appearing in the textual reviews written by previous customers for those products. Then, starting from the textual query of a user’s request for recommendation, our ap...