A quality based recommender system to disseminate information in a university digital library

Recommender systems evaluate and filter the great amount of information available on the Web, so they could be used in an academic environment to help users in their searches of relevant information. In the literature, a lot of approaches for generating personalized recommendations of information items in such environment can be found. Usually, these approaches use user profiles and/or features of items to predict those relevant items, but they do not take into account the quality of these items. To overcome this problem, in this paper we propose a new recommender system based on quality. This system uses the quality of the items to estimate their relevance. The system measures the item quality and takes into account this measure like a new factor to be considered in the recommendation process. In such a way, we present a recommender system based on items' quality, to help users to access relevant research resources. This recommender systems is developed by using a fuzzy linguistic approach and it has been tested satisfactorily in a university digital library.

[1]  Francisco Herrera,et al.  A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[3]  Hungyune Chao,et al.  Assessing the Quality of Academic Libraries on the Web: The Development and Testing of Criteria. , 2002 .

[4]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[5]  F. Herrera,et al.  A consistency-based procedure to estimate missing pairwise preference values , 2008 .

[6]  Enrique Herrera-Viedma,et al.  A review of quality evaluation of digital libraries based on users’ perceptions , 2012, J. Inf. Sci..

[7]  Francisco Herrera,et al.  Group decision making with incomplete fuzzy linguistic preference relations , 2009, Int. J. Intell. Syst..

[8]  Peretz Shoval,et al.  Information Filtering: Overview of Issues, Research and Systems , 2001, User Modeling and User-Adapted Interaction.

[9]  Edward A. Fox,et al.  Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries , 2004, TOIS.

[10]  Francisco Herrera,et al.  A fusion approach for managing multi-granularity linguistic term sets in decision making , 2000, Fuzzy Sets Syst..

[11]  Gerhard Weikum,et al.  Social Wisdom for Search and Recommendation , 2008, IEEE Data Eng. Bull..

[12]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[13]  Long-Sheng Chen,et al.  Developing recommender systems with the consideration of product profitability for sellers , 2008, Inf. Sci..

[14]  Macarena Espinilla,et al.  Improving the Effectiveness of Knowledge Based Recommender Systems Using Incomplete Linguistic Preference Relations , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[16]  Gobinda G. Chowdhury,et al.  Introduction to Digital Libraries , 2002 .

[17]  Robert R. Korfhage,et al.  Information Storage and Retrieval , 1963 .

[18]  Enrique Herrera-Viedma,et al.  Evaluating the information quality of Web sites: A methodology based on fuzzy computing with words: Special Topic Section on Soft Approaches to Information Retrieval and Information Access on the Web , 2006 .

[19]  Anthony F. Norcio,et al.  Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems , 2009, Fuzzy Sets Syst..

[20]  Pei-yu Chen,et al.  Does Collaborative Filtering Technology Impact Sales? Empirical Evidence from Amazon.Com , 2007 .

[21]  Enrique Herrera-Viedma,et al.  Evaluating the informative quality of documents in SGML format from judgements by means of fuzzy linguistic techniques based on computing with words , 2003, Inf. Process. Manag..

[22]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[23]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[24]  由希 辻.  Representation , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[25]  Francisco Herrera,et al.  Aggregation operators for linguistic weighted information , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[26]  Enrique Herrera-Viedma,et al.  A fuzzy linguistic model to evaluate the quality of Library 2.0 functionalities , 2013, Int. J. Inf. Manag..

[27]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[28]  Yukun Cao,et al.  An intelligent fuzzy-based recommendation system for consumer electronic products , 2007, Expert Syst. Appl..

[29]  Juan C. Burguillo,et al.  A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition , 2010, Inf. Sci..

[30]  Francisco Herrera,et al.  h-Index: A review focused in its variants, computation and standardization for different scientific fields , 2009, J. Informetrics.

[31]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[32]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[33]  Gary Marchionini,et al.  Evaluating Digital Libraries: A Longitudinal and Multifaceted View , 2000, Libr. Trends.

[34]  Man Ieee Systems.  IEEE transactions on systems, man and cybernetics , 1971 .

[35]  Alan F. Smeaton,et al.  Joint DELOS-NSF workshop on personalisation and recommender systems in digital libraries , 2001, SIGF.

[36]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[37]  Duen-Ren Liu,et al.  A hybrid of sequential rules and collaborative filtering for product recommendation , 2009, Inf. Sci..

[38]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[39]  L. Ross,et al.  The Library is Dead, Long Live the Library! The Practice of Academic Librarianship and the Digital Revolution , 2008 .

[40]  Enrique Herrera-Viedma,et al.  Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries , 2010, Knowl. Based Syst..

[41]  Alan F. Smeaton,et al.  Personalisation and recommender systems in digital libraries , 2005, International Journal on Digital Libraries.

[42]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[43]  Judy H. Jeng.  What is usability in the context of the digital library and how can it be measured , 2005 .

[44]  E. Herrera‐Viedma,et al.  Libraries' social role in the information age. , 2013, Science.

[45]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[46]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[47]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[48]  L. Ross,et al.  The Library is , Dead , L ' ong Live the Library ! The Practice of , Academic Librarianship and the Digital evolution , 2008 .

[49]  Mei-Hua Hsu,et al.  A personalized English learning recommender system for ESL students , 2008, Expert Syst. Appl..

[50]  Anne Morris,et al.  The problem of information overload in business organisations: a review of the literature , 2000, Int. J. Inf. Manag..

[51]  Enrique Herrera-Viedma,et al.  Evaluating the information quality of Web sites: A methodology based on fuzzy computing with words , 2006, J. Assoc. Inf. Sci. Technol..

[52]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[53]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[54]  Abraham Kandel,et al.  Search engines, link analysis, and user's Web behavior , 2008 .

[55]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[56]  Umberto Straccia,et al.  A personalized collaborative Digital Library environment: a model and an application , 2005, Inf. Process. Manag..

[57]  Enrique Herrera-Viedma,et al.  A multi-disciplinar recommender system to advice research resources in University Digital Libraries , 2009, Expert Syst. Appl..

[58]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[59]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[60]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[61]  Mark Levene,et al.  Search Engines: Information Retrieval in Practice , 2011, Comput. J..

[62]  Enrique Herrera-Viedma,et al.  A Model Based on Fuzzy Linguistic Information to Evaluate the Quality of Digital Libraries , 2010, Int. J. Inf. Technol. Decis. Mak..

[63]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[64]  C. Lee Giles,et al.  Searching the Web: general and scientific information access , 1999, First IEEE/POPOV Workshop on Internet Technologies and Services. Proceedings (Cat. No.99EX391).

[65]  David S. Carter,et al.  Unobtrusive Data Analysis of Digital Reference Questions and Service at the Internet Public Library: An Exploratory Study , 2000, Libr. Trends.

[66]  J. Wootton,et al.  Search engines. , 1997, Journal of women's health.