mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Implementation of Recommender System Using Feature-Based Sentiment Analysis

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Collaborative Filtering (CF)-based recommendation technique helps people to make choices based on the opinions of other people who share similar interests. This technique has been suffering from the problems of data sparsity and cold start because of insufficient user ratings or absence of data about users or items. This can affect the accuracy of the recommendation system. User-generated reviews are a plentiful source of user opinions and interests. The proposed personalized recommendation model uses feature base sentiment analysis using ontology that extracts the semantically related features to find the users’ individual preferences rather than rating scores in order to build user profiles that can be understood by user-based collaborative filtering recommendation model. The proposed model intends to alleviate data sparsity problem and to improve accuracy of recommender system by finding user preferences from review text

Personalized large scale classification of public tenders on hadoop

Ce projet a été réalisé dans le cadre d’un partenariat entre Fujitsu Canada et Université Laval. Les besoins du projets ont été centrés sur une problématique d’affaire définie conjointement avec Fujitsu. Le projet consistait à classifier un corpus d’appels d’offres électroniques avec une approche orienté big data. L’objectif était d’identifier avec un très fort rappel les offres pertinentes au domaine d’affaire de l’entreprise. Après une séries d’expérimentations à petite échelle qui nous ont permise d’illustrer empiriquement (93% de rappel) l’efficacité de notre approche basé sur l’algorithme BNS (Bi-Normal Separation), nous avons implanté un système complet qui exploite l’infrastructure technologique big data Hadoop. Nos expérimentations sur le système complet démontrent qu’il est possible d’obtenir une performance de classification tout aussi efficace à grande échelle (91% de rappel) tout en exploitant les gains de performance rendus possible par l’architecture distribuée de Hadoop.This project was completed as part of an innovation partnership with Fujitsu Canada and Université Laval. The needs and objectives of the project were centered on a business problem defined jointly with Fujitsu. Our project aimed to classify a corpus of electronic public tenders based on state of the art Hadoop big data technology. The objective was to identify with high recall public tenders relevant to the IT services business of Fujitsu Canada. A small scale prototype based on the BNS algorithm (Bi-Normal Separation) was empirically shown to classify with high recall (93%) the public tender corpus. The prototype was then re-implemented on a full scale Hadoop cluster using Apache Pig for the data preparation pipeline and using Apache Mahout for classification. Our experimentation show that the large scale system not only maintains high recall (91%) on the classification task, but can readily take advantage of the massive scalability gains made possible by Hadoop’s distributed architecture

Engineering an Open Web Syndication Interchange with Discovery and Recommender Capabilities

Web syndication has become a popular means of delivering relevant information to people online but the complexity of standards, algorithms and applications pose considerable challenges to engineers.  This paper describes the design and development of a novel Web-based syndication intermediary called InterSynd and a simple Web client as a proof of concept. We developed format-neutral middleware that sits between content sources and the user. Additional objectives were to add feed discovery and recommendation components to the intermediary. A search-based feed discovery module helps users find relevant feed sources. Implicit collaborative recommendations of new feeds are also made to the user. The syndication software built uses open standard XML technologies and the free open source libraries. Extensibility and re-configurability were explicit goals. The experience shows that a modular architecture can combine open source modules to build state-of-the-art syndication middleware and applications. The data produced by software metrics indicate the high degree of modularity retained