1,721,060 research outputs found
Language Games: Solving the Vocabulary Problem in Multi-Case-Base Reasoning
No full textThe problem of heterogeneous case representation poses a major obstacle to realising real-life multi-case-base CBR systems. The knowledge overhead in developing and maintaining translation protocols between distributed case bases poses a serious challenge to CBR developers. In this paper, we situate CBR as a flexible problem-solving strategy that relies on several heterogeneous knowledge containers. We introduce a technique called language games to solve the interoperability issue. Our technique has two phases. The first is an eager learning phase where case bases communicate to build a shared indexing lexicon of similar cases in the distributed network. The second is the problem-solving phase where, using the distributed index, a case base can quickly consult external case bases if the local solution is insufficient. We provide a detailed description of our approach and demonstrate its effectiveness using an evaluation on a real data set from the tourism domai
An on-line Evaluation Framework for Recommender Systems
No full textSeveral techniques are currently used to evaluate recommender systems. These techniques involve off-line analysis using evaluation methods from machine learning and information retrieval. We argue that while off-line analysis is useful, user satisfaction with a
recommendation strategy can only be measured in an on-line context. We propose a new evaluation framework which involves a paired test of two recommender systems which simultaneously compete to give the best recommendations to the same user at the same time. The user interface and the interaction model for each system is the same. The framework enables you to specify an API so that different recommendation strategies may take part in such a competition. The API defines issues such as access to data, the interaction model and the means of gathering positive feedback from the user. In this way it is possible to obtain a relative measure of user satisfaction with the two system
Re-using Implicit Knowledge in Short-Term Information Profiles for Context-Sensitive Tasks
No full textTypically, case-based recommender systems recommend single items to the on-line customer. In this paper we introduce the idea of recommending a user-defined collection of items where the user has implicitly encoded the relationships between the items. Automated collaborative filtering (ACF), a so-called "contentless" technique, has been widely used as a recommendation strategy for music items. However, its reliance on a global model of the user’s interests makes it unsuited to catering for the user’s local interests. We consider the context-sensitive task of building a compilation, a user-defined collection of music tracks. In our analysis, a collection is a case that captures a specific short-term information/music need. In an offline evaluation, we demonstrate how a case-completion strategy that uses short-term representations is significantly more effective than the ACF technique. We then consider the problem of recommending a compilation according to the user’s most recent listening preferences. Using a novel on-line evaluation where two algorithms compete for the user’s attention, we demonstrate how a knowledge-light case-based reasoning strategy successfully addresses this proble
Learning Contextualised Weblog Topics
No full textThe blogosphere refers to the distributed network of user opinions published on the WWW. Whereas centralized review sites such Amazon.com previously allowed users to post opinions on goods such as books and CDs, blogging software allows users to publish opinions on any topic without constraints on predefined schema. However, centralized review sites such as Amazon.com have one significant advantage: reviews pertaining to a single topic are collected together in one place, allowing readers to peruse a diverse range of opinions quickly. In this paper we examine how such a topiccentric view of the Blogosphere can be created. We characterise the problems in aligning similar concepts created by a set of distributed, autonomous users and describe current initiatives to solve the problem. Finally, we introduce the Tagsocratic project, a novel initiative to solve the concept alignment problem using techniques derived from research in language acquisition among distributed, autonomous agents. 1
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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