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Analysis of institutional authors

Jorro-Aragoneses J.l.Author

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Proceedings Paper

A hybrid CBR approach for the long tail problem in recommender systems

Publicated to:Lecture Notes In Computer Science. 10339 LNAI 35-45 - 2017-01-01 10339 LNAI(), DOI: 10.1007/978-3-319-61030-6_3

Authors: Alshammari G; Jorro-Aragoneses J; Kapetanakis S; Petridis M; Recio-García J; Díaz-Agudo B

Affiliations

Middlesex University - Author
Universidad Complutense de Madrid - Author
University of Brighton - Author

Abstract

Recommender systems is an important tool to help users find relevant items to their interests in a variety of products and services including entertainment, news, research articles, and others. Recommender systems generate lists of recommendations/suggestions based on information from past user interactions, choices, demographic information as well as using machine learning and data mining. The most popular techniques for generating recommendations are through content-based and collaborative filtering with the latter used to provide user to user recommendations. However, collaborative filtering suffers from the long tail problem, i.e., it does not work correctly with items that contain a small number of ratings over large item populations with respectively large numbers of ratings. In this paper, we propose a novel approach towards addressing the long tail recommendation problem by applying Case-based Reasoning on “user history” to predict the rating of newly seen items which seem to belong to the long tail. We present a hybrid approach and a framework implemented with jCOLIBRI to evaluate it using the freely available Movielens dataset [8]. Our results seem promising and they seem to improve the existing prediction outcomes from the available literature.

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Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.74, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-20, the following number of citations:

  • WoS: 13
  • Scopus: 20
  • OpenCitations: 9

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-20:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 17.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 17 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.85.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (JORRO ARAGONESES, JOSE LUIS).