Committee-Based Profiles for Politician Finding

One step towards breaking down barriers between citizens and politicians is to help people identify those politicians who share their concerns. This paper is set in the field of expert finding and is based on the automatic construction of politicians’ profiles from their speeches on parliamentary committees. These committee-based profiles are treated as documents and are indexed by an information retrieval system. Given a query representing a citizen’s concern, a profile ranking is then obtained. In the final step, the different results for each candidate are combined in order to obtain the final politician ranking. We explore the use of classic combination strategies for this purpose and present a new approach that improves state-of-the-art performance and which is more stable under different conditions. We also introduce a two-stage model where the identification of a broader concept (such as the committee) is used to improve the final politician ranking.This work has been funded by the Spanish Ministerio de Economı́a y Competitividad under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Use of topical and temporal profiles and their hybridisation for content-based recommendation

In the context of content-based recommender systems, the aim of this paper is to determine how better profiles can be built and how these affect the recommendation process based on the incorporation of temporality, i.e. the inclusion of time in the recommendation process, and topicality, i.e. the representation of texts associated with users and items using topics and their combination. The main contribution of the paper is to present two different ways of hybridising these two dimensions and to evaluate and compare them with other alternatives

Information Retrieval and Machine Learning Methods for Academic Expert Finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Spanish “Agencia Estatal de Investigación” under grants PID2019-106758GB-C31 and PID2020-113230RB-C22Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under grant A-TIC-146-UGR20European Regional Development Fund (ERDF-FEDER

Information retrieval and machine learning methods for academic expert finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Agencia Estatal de Investigación | Ref. PID2019-106758GB-C31Agencia Estatal de Investigación | Ref. PID2020-113230RB-C22FEDER/Junta de Andalucía | Ref. A-TIC-146-UGR2

Automatic Construction of Multi-faceted User Profiles using Text Clustering and its Application to Expert Recommendation and Filtering Problems

In the information age we are living in today, not only are we interested in accessing multimedia objects such as documents, videos, etc. but also in searching for professional experts, people or celebrities, possibly for professional needs or just for fun. Information access systems need to be able to extract and exploit various sources of information (usually in text format) about such individuals, and to represent them in a suitable way usually in the form of a profile. In this article, we tackle the problems of profile-based expert recommendation and document filtering from a machine learning perspective by clustering expert textual sources to build profiles and capture the different hidden topics in which the experts are interested. The experts will then be represented by means of multi-faceted profiles. Our experiments show that this is a valid technique to improve the performance of expert finding and document filtering