Search results for "recommender system"
showing 10 items of 70 documents
Hints from the Crowd: A Novel NoSQL Database
2013
The crowd can be an incredible source of information. In particular, this is true for reviews about products of any kind, freely provided by customers through specialized web sites. In other words, they are social knowledge, that can be exploited by other customers. The Hints From the Crowd HFC prototype, presented in this paper, is a NoSQL database system for large collections of product reviews; the database is queried by expressing a natural language sentence; the result is a list of products ranked based on the relevance of reviews w.r.t. the natural language sentence. The best ranked products in the result list can be seen as the best hints for the user based on crowd opinions the revi…
An adaptive approach to learning the preferences of users in a social network using weak estimators
2012
Published version of an article in the journal: Journal of Information Processing Systems. Also available from the publisher at: http://dx.doi.org/10.3745/JIPS.2012.8.2.191 - Open Access Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked i…
Quality Improvement Based on Big Data Analysis
2016
Big data analysis has become an important trend in computer science. Quality improvement is a constant in current industry trends. In this paper, we present an idea of quality improvement based on big data analysis with the aid of linked data and ontologies in order to implement it in the case of automotive parts production. We consider defective automotive products and try to find the best refurbishment solution for them considering their characteristics. Moreover, we propose to develop a recommender system that is able to give recommendations in order to prevent or to alleviate defects and to provide insights for possible causes that led to these defective parts. This study intends to hel…
A Pseudo-Supervised Approach to Improve a Recommender Based on Collaborative Filtering
2003
This PhD Thesis develops an optimal recommender. First of all, users accessing to a Web site are clustered. If a user belongs to a cluster, the system offers services which are usually accessed by users from the same cluster in a collaborative filtering scheme. A novel approach based on a users simulator and a dynamic recommendation system is proposed. The simulator is used to create the situations that one can find in a Web site. Introduction of dynamics in the recommender allows to change the clusters and in turn, the decisions which are taken. Since the system is based both on supervised and unsupervised learning whose borders are not too clear in our approach, we talk about a pseudo-sup…
A service-based recommendation system to assist decision making in a small and medium company
2020
Collaboration and resource sharing have attracted a great interest among companies. Our contribution relates to this context, providing a decision-making support for companies. We propose a platform to facilitate resource sharing between collaborative companies, through a recommendation system. This system provides similar services that respond to a company’s needs. The approach presented in this paper includes the choice of a representation model for the services as well as the selection of an appropriate measure of similarity. This latter is mainly based on a comparative study of three measures of similarity using several performance evaluation measures. In order to evaluate our approach,…
EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
2021
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user’s digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals’ computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then …
Exploiting community detection to recommend privacy policies in decentralized online social networks
2018
The usage of Online Social Networks (OSNs) has become a daily activity for billions of people that share their contents and personal information with the other users. Regardless of the platform exploited to provide the OSNs’ services, these contents’ sharing could expose the OSNs’ users to a number of privacy risks if proper privacy-preserving mechanisms are not provided. Indeed, users must be able to define its own privacy policies that are exploited by the OSN to regulate access to the shared contents. To reduce such users’ privacy risks, we propose a Privacy Policies Recommended System (PPRS) that assists the users in defining their own privacy policies. Besides suggesting the most appro…
Designing Ontology-Driven Recommender Systems for Tourism
2014
Nowadays, Internet users may experience some difficulty in finding the information they need from the huge multitude of existing web pages. A possible solution to this problem might lie in delegating some of the search tasks to machines, or in other words, in building a Semantic Web in which information could be processed automatically by intelligent software agents. Given the constantly increasing growth of the tourism industry, it might be particularly helpful to develop virtual assistants capable of planning trips on the basis of a user’s interests. If so, adopting Semantic Web technologies would make it possible to provide a more customized service to each user and thus satisfy their re…
Hybrid recommendation methods in complex networks
2015
We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a …
WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach
2020
The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture - called WhoSNext (WSN) - tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a …