Prediction of users’ future requests using neural network

With the rapid growth of the World Wide Web, finding useful information from the Internet has become a critical issue. Automatic classification of user navigation patterns provides a useful tool to solve these problems. In this paper, we propose an approach for classification of users’ navigation patterns and prediction of users’ future requests. Users’ profiles are constructed based on Web log server files and one of clustering methods is implemented to users’ profiles for assigning navigation patterns. Finally, using neural network, recommender engine produces a relevant recommendation list of web pages to the active user. The preliminary results indicate that the proposed approach has high accuracy and coverage in prediction of users’ future requests

Mining the Web Data for Classifying and Predicting Users’ Requests

Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client’s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations

Proceso de identificación de comportamiento de comunidades educativas basado en resultados académicos

Los procesos de explotación de información se incorporan al ámbito educativo para ayudar a entender y mejorar tanto la enseñanza de los docentes como el aprendizaje de los alumnos. Entre estas cuestiones se destaca el análisis del comportamiento de comunidades educativas de forma tal de proveer al docente herramientas que ayuden a mejorar la enseñanza/aprendizaje. En este contexto, el presente trabajo de tesis tiene como objetivo proponer, estudiar y validar un proceso de explotación de información que permita identificar el comportamiento de comunidades educativas basado en resultados académicos.The data mining processes are incorporated to the educational environment to help to understand and improve the way teachers teach, and the way students learn. These issues stands behavioral analysis of learning communities in such a way to provide teachers tools to help improve understanding of the processes of teaching and learning. In this context, this thesis aims to propose, study and validate a data mining process to identify the behavior of educational communities, based on academic results.Facultad de Informátic

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology