Twelve Theses on Reactive Rules for the Web

Reactivity, the ability to detect and react to events, is an essential functionality in many information systems. In particular, Web systems such as online marketplaces, adaptive (e.g., recommender) systems, and Web services, react to events such as Web page updates or data posted to a server. This article investigates issues of relevance in designing high-level programming languages dedicated to reactivity on the Web. It presents twelve theses on features desirable for a language of reactive rules tuned to programming Web and Semantic Web applications

A Distributed and Accountable Approach to Offline Recommender Systems Evaluation

Different software tools have been developed with the purpose of performing offline evaluations of recommender systems. However, the results obtained with these tools may be not directly comparable because of subtle differences in the experimental protocols and metrics. Furthermore, it is difficult to analyze in the same experimental conditions several algorithms without disclosing their implementation details. For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion. By relying on consolidated web protocols, we created RESTful APIs for training and querying recommenders remotely. In this way, it is possible to easily integrate into the same toolkit algorithms realized with different technologies. In details, the experimenter can perform an evaluation by simply visiting a web interface provided by RecLab. The framework will then interact with all the selected recommenders and it will compute and display a comprehensive set of measures, each representing a different metric. The results of all experiments are permanently stored and publicly available in order to support accountability and comparative analyses.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System

Recommender Systems for the Semantic Web

This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system in the proposed architecture to represent the problem domain, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. In order to allow for such dynamic behaviour, the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items

Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions

Heleou, S., Drachsler, H., & Gillet, D. (2009). Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions. 1st workshop on Context-aware Recommender Systems for Learning at the Alpine Rendez-Vous. November, 30-December, 3, 2009, Garmisch-Patenkirchen, Germany.This paper discusses challenges and possible solutions of recommender systems for Technology-Enhanced Learning (TEL). It also briefly presents the the 3A contextual recommender system and explores its applicability and evaluation in the context of learners using multiple Web 2.0 applications