Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Building emergent social networks and group profiles by semantic user preference clustering

This is an electronic version of the paper presented at the International Workshop on Semantic Network Analysis (SNA 2006) at the European Semantic Web Conference (ESWC 2006), held in Budva on 2006This paper presents a novel approach to automatic semantic social network construction based on semantic user preference clustering. Considering a number of users, each of them with an associated ontology-based profile, we propose a strategy that clusters the concepts of the reference ontology according to user preferences of these concepts, and then determines which clusters are more appropriate to the users. The resultant user clusters can be merged into individual group profiles, automatically defining a semantic social network suitable for use in collaborative and recommendation environments.This research was supported by the European Commission (FP6-027685 – MESH), and the Spanish Ministry of Science and Education (TIN2005-06885). The expressed content is the view of the authors but not necessarily the view of the MESH project as a whole

Effects of competition in education: A case study in an e-learningenvironment

This is an electronic version of the paper presented at the IADIS Multi Conference on Computer Science and Information Systems, held in Freiburg on 2010We analyse the effects of competition in education. We identify the benefits and drawbacks of forcing students to compete themselves during their learning process, and investigate a number of features a competitive learning activity should have in order to motivate students, and improve their academic performance. More specifically, by using a simple Web system, we conduct a competition undertaken for a symbolic value, performed in a short period of time, and characterised by all participants feeling like they have a chance to win. Following these principles, empirical results with 77 students show that a balance between competition and cooperation is achieved, and the focus on the learning goals instead of on the competition itself remains

Parallel Perceptrons and Training Set Selection for Imbalanced Classification Problems

This is an electronic version of the paper presented at the Learning 2004, held in Spain on 2004Parallel perceptrons are a novel approach to the study of committee machines that allows, among other things, for a fast training with minimal communications between outputs and hidden units. Moreover, their training allows to naturally de¯ne margins for hidden unit activations. In this work we shall show how to use those margins to perform subsample selections over a given training set that reduce training complexity while enhancing classi¯cation accuracy and allowing for a balanced classi¯er performance when class sizes are greatly di®erent.With partial support of Spain's CICyT, TIC 01-57

On the exploitation of user personality in recommender systems

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of the First International Workshop on Decision Making and Recommender Systems (DMRS2014)In this paper we revise the state of the art on personality-aware recommender systems, identifying main research trends and achievements up to date, and discussing open issues that may be addressed in the future.This work was supported by the Spanish Ministry of Science and Innovation (TIN2013-47090-C3-2)

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Self-adjusting hybrid recommenders based on social network analysis

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, http://dx.doi.org/10.1145/2009916.2010092Ensemble recommender systems successfully enhance recom-mendation accuracy by exploiting different sources of user prefe-rences, such as ratings and social contacts. In linear ensembles, the optimal weight of each recommender strategy is commonly tuned empirically, with limited guarantee that such weights are optimal afterwards. We propose a self-adjusting hybrid recommendation approach that alleviates the social cold start situation by weighting the recommender combination dynamically at recommendation time, based on social network analysis algorithms. We show empirical results where our approach outperforms the best static combination for different hybrid recommenders.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), University Autónoma de Madrid and the Community of Madrid (CCG10-UAM/TIC-5877)

On the Extraction and Use of Arguments in Recommender Systems: A Case Study in the E-participation Domain

In this paper, we present ongoing work on the automatic extraction of arguments from textual content, and on the use of interconnected argument structures by recommender systems. Differently to the majority of existing argument mining methods –which only consider ‘premise’ and ‘claim’ as the components of an argument, and ‘support’ and ‘attack’ as the possible relations between argument components–, we propose an argumentation model based on a detailed taxonomy of argumentative relations. Moreover, we provide a lexicon of English and Spanish linguistic connectors categorized in our taxonomy. As a proof of concept, we apply a simple, yet effective method that makes use of the built taxonomy and lexicon to extract argument graphs from citizen proposals and debates of an e-participation platform. We then describe how the extracted graphs could be exploited to generate and explain argument-based recommendationsThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00

Statistical biases in Information Retrieval metrics for recommender systems

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposesThis work was partially supported by the national Spanish Government (grants nr. TIN2013-47090-C3-2 and TIN2016-80630-P). We wish to express our gratitude to the anonymous reviewers whose insightful and generous feedback guided us in producing an enhanced version of the paper beyond the amendmentof flaws and shortcoming

CORE: a tool for collaborative ontology reuse and evaluation

Ontology evaluation can be defined as assessing the quality and the adequacy of an ontology for being used in a specific context, for a specific goal. In this work, a tool for Collaborative Ontology Reuse and Evaluation (CORE) is presented. The system receives an informal description of a semantic domain and determines which ontologies, from an ontology repository, are the most appropriate to describe the given domain. For this task, the environment is divided into three main modules. The first component receives the problem description represented as a set of terms and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository and determine which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques that combine the selected criteria. A third component of the system uses manual user evaluations of the ontologies in order to incorporate a human, collaborative assessment of the quality of ontologies