743 research outputs found
May I Suggest? Comparing Three PLE Recommender Strategies
Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general
Collaborative-demographic hybrid for financial: product recommendation
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM
processes, several financial institutions are striving to leverage customer data and integrate insights
regarding customer behaviour, needs, and preferences into their marketing approach. As decision
support systems assisting marketing and commercial efforts, Recommender Systems applied to the
financial domain have been gaining increased attention. This thesis studies a Collaborative-
Demographic Hybrid Recommendation System, applied to the financial services sector, based on real
data provided by a Portuguese private commercial bank. This work establishes a framework to support
account managers’ advice on which financial product is most suitable for each of the bank’s corporate
clients. The recommendation problem is further developed by conducting a performance comparison
for both multi-output regression and multiclass classification prediction approaches. Experimental
results indicate that multiclass architectures are better suited for the prediction task, outperforming
alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass
Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming
algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving
corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study
provides important contributions for positioning the bank’s commercial efforts around customers’
future requirements. By allowing for a better understanding of customers’ needs and preferences, the
proposed Recommender allows for more personalized and targeted marketing contacts, leading to
higher conversion rates, corporate profitability, and customer satisfaction and loyalty
Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes
Recommender systems are an increasingly important technology and researchers have recently argued for incorporating different kinds of data to improve recommendation quality. This paper presents a novel approach to generating recommendations and evaluates its effectiveness. First, we review evidence that item viewing time can reveal user preferences for items. Second, we model item preference as a weighted function of preferences for item attributes. We then propose a method for generating recommendations based on these two propositions. The results of a laboratory evaluation show that the proposed approach generated estimated item ratings consistent with explicit item ratings and assigned high ratings to products that reflect revealed preferences of users. We conclude by discussing implications and identifying areas for future research
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