Reducing offline evaluation bias of collaborative filtering algorithms

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

Desktop multimedia environments to support collaborative distance learning

Desktop multimedia conferencing, when two or more persons can communicate among themselves via personal computers with the opportunity to see and hear one another as well as communicate via text messages while working with commonly available stored resources, appears to have important applications to the support of collaborative learning. In this paper we explore this potential in three ways: (a) through an analysis of particular learner needs when learning and working collaboratively with others outside of face-to-face situations; (b) through an analysis of different forms of conferencing environments, including desktop multimedia environments, relative to their effectiveness in terms of meeting learner needs for distributed collaboration; and (c) through reporting the results of a formative evaluation of a prototype desktop multimedia conferencing system developed especially for the support of collaborative learning. Via these analyses, suggestions are offered relating to the functionalities of desktop multimedia conferencing systems for the support of collaborative learning, reflecting new developments in both the technologies available for such systems and in our awareness of learner needs when working collaboratively with one other outside of face-to-face situations

Distributed intrusion detection trust management through integrity and expertise evaluation

Information sharing and collaboration has facilitated decision accuracy and reaction time in many applications. Distributed Intrusion Detection Systems (DIDS) solutions are one of such applications that have dramatically been transformed. This is mainly due to increasing number of attacks as well as sophisticated nature of today's intrusions. Moreover, it has been shown that various critical components of a system can be targeted. This is further exasperated by the fact that most DIDS models do not consider attacks that targets the collaborative network itself. We specifically find this issue to be very critical and hence in this paper we propose a trust aware DIDS simulation model that is capable of categorizing each participating IDS expertise (i.e. speciality and competence), therefore helps collaborating organizations to consult our simulation model for choosing the right candidate for any type of intrusion. We call our proposed DIDS model Consultative Trusted Computing-based Collaborative IDS (CTC IDS). We utilize the Trusted Platform Module (TPM) for integrity evaluation and to fine-tune peer evaluation

Fisheries policies for a new era

This Guidance Note presents a simple approach to analyzing the governance context for development of aquatic agricultural systems; it is intended as an aid to action research, and a contribution to effective program planning and evaluation. It provides a brief introduction to the value of assessing governance collaboratively, summarizes an analytical framework, and offers practical guidance on three stages of the process: identifying obstacles and opportunities, debating strategies for influence, and planning collaborative actions

LBWiki: A Location-Based Wiki

Wiki systems provide a simple interface paradigm that allow non-technical users to author collaborative on-line hypertexts. In this paper we propose to use the same simple paradigm to allow users to create content for ubiquitous information systems, and present LBWiki, a prototype location-based Wiki that allows users with a mobile device to create Wiki pages based on GPS co-ordinates. We describe the hierarchical location scheme used within LBWiki and the results of a small evaluation, in which users reacted positively to the concept, but asked for greater control over geographical regions, and highlighted the importance of accurate location technology

Towards evaluation of personalized and collaborative information retrieval

We propose to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by gathering additional meta-information during the topic (query) development process. We propose a controlled query generation process with activity logging for each topic developer. The standard ad-hoc collection will thus be accompanied by a new set of thematically related topics and the associated log information, and has the potential to simulate a real-world search scenario to encourage retrieval systems to mine user information from the logs to improve IR effectiveness. The proposed methodology described in this paper will be applied in a pilot task which is scheduled to run in the FIRE 2011 evaluation campaign. The task aims at investigating the research question of whether personalized and collaborative IR retrieval experiments and evaluation can be pursued by enriching a standard ad-hoc collection with such meta-information

Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision