RACOFI: A Rule-Applying Collaborative Filtering System

In this paper we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at http://racofi.elg.ca.

A fuzzy tree similarity based recommendation approach for telecom products

Due to the huge product assortments and complex descriptions of telecom products, it is a great challenge for customers to select appropriate products. A fuzzy tree similarity based hybrid recommendation approach is proposed to solve this issue. In this study, fuzzy techniques are used to deal with the various uncertainties existing within the product and customer data. A fuzzy tree similarity measure is developed to evaluate the semantic similarity between tree structured products or user profiles. The similarity measures for items and users both integrate the collaborative filtering (CF) and semantic similarities. The final recommendation hybridizes item-based and user-based CF recommendation techniques. A telecom product recommendation case study is given to show the effectiveness of the proposed approach. © 2013 IEEE

A similarity measure on tree structured business data

In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes&rsquo; concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.<br /

Similarity measure models and algorithms for hierarchical cases

Many business situations such as events, products and services, are often described in a hierarchical structure. When we use case-based reasoning (CBR) techniques to support business decision-making, we require a hierarchical-CBR technique which can effectively compare and measure similarity between two hierarchical cases. This study first defines hierarchical case trees (HC-trees) and discusses related features. It then develops a similarity evaluation model which takes into account all the information on nodes' structures, concepts, weights, and values in order to comprehensively compare two hierarchical case trees. A similarity measure algorithm is proposed which includes a node concept correspondence degree computation algorithm and a maximum correspondence tree mapping construction algorithm, for HC-trees. We provide two illustrative examples to demonstrate the effectiveness of the proposed hierarchical case similarity evaluation model and algorithms, and possible applications in CBR systems. © 2011 Elsevier Ltd. All rights reserved

Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation

Learning objects strive for reusability in e-Learning to reduce cost and allow personalization of content. We argue that learning objects require adapted Information Retrieval systems. In the spirit of the Semantic Web, we discuss the semantic description, discovery, and composition of learning objects using Web-based MP3 objects as examples. As part of our project, we tag learning objects with both objective and subjective metadata. We study the application of collaborative filtering as prototyped in the RACOFI (Rule-Applying Collaborative Filtering) Composer system, which consists of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We are currently developing RACOFI to generate context-aware recommendation lists. Context is handled by multidimensional predictions produced from a database-driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The prototype is available at inDiscover.net

Fuzzy measures on the Gene Ontology for gene product similarity

pre-printOne of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum

Analyzing and Implementing a Feature Mapping Approach to CAD System Interoperability

Interoperable information exchange between computer-aided design (CAD) systems is one of the major problems to enable information integration in a collaborative engineering environment. Although a significant amount of work has been done on the extension and standardization of CAD data formats as well as the cooperation of CAD systems in both academy and industry, these approaches are generally low-level and narrowly targeted. Lack of fundamental study of interoperability and generic solution to this problem is the major issue. Our intention of this research is to design a solution of CAD feature interoperability as generic as possible based on a theoretical foundation of language types. In this paper, we present a fundamental model of semantic features and feature mapping process based on the type theory. We implement and demonstrate our approach for automated feature exchange between commercial CAD systems

A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in E-Business Environments

A tree similarity algorithm for match-making of agents in e-Business environments is presented. Product/service descriptions of seller and buyer agents are represented as node-labelled, arc-labelled, arc-weighted trees. A similarity algorithm for such trees is developed as the basis for semantic match-making in a virtual marketplace. The trees are exchanged using an XML serialization in Object-Oriented RuleML. Correspondingly, we use the declarative language Relfun to implement the similarity algorithm as a parameterised, recursive functional program. Three main recursive functions perform a top-down traversal of trees and the bottom-up computation of similarity. Results from our experiments aiming to match buyers and sellers are found to be effective and promising for e-Business/e-Learning environments. The algorithm can be applied in all environments where weighted trees are used.Nous pr\ue9sentons ici un algorithme de similarit\ue9 arborescent pour l'appariement des agents dans les environnements de commerce \ue9lectronique. Les descriptions des produits et des services offerts par les agents vendeurs et acheteurs sont repr\ue9sent\ue9es par des arbres \ue0 n\u153uds \ue9tiquet\ue9s, \ue0 arcs \ue9tiquet\ue9s et \ue0 arcs pond\ue9r\ue9s. Nous \ue9laborons un algorithme de similarit\ue9 pour les arbres de ce type qui sert de base pour l'appariement s\ue9mantique dans une place de march\ue9 virtuelle. Les arbres sont \ue9chang\ue9s au moyen d'une s\ue9rialisation XML dans le langage orient\ue9 objet RuleML. De fa\ue7on correspondante, nous nous servons du langage d\ue9claratif Relfun pour mettre en \u153uvre l'algorithme de similarit\ue9 sous la forme d'un programme fonctionnel r\ue9cursif param\ue9tr\ue9. Trois fonctions r\ue9cursives principales r\ue9alisent une travers\ue9e descendante des arbres ainsi que le calcul ascendant des similarit\ue9s. Nous avons constat\ue9 que les r\ue9sultats de nos exp\ue9riences visant \ue0 apparier les acheteurs et les vendeurs \ue9taient efficaces et prometteurs pour les environnements d'apprentissage \ue9lectronique et de commerce \ue9lectronique. Cet algorithme peut \ueatre appliqu\ue9 dans tous les environnements o\uf9 des arbres pond\ue9r\ue9s sont utilis\ue9s.NRC publication: Ye