Difference between revisions of "Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems"

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[[Category:Scientific works]]

Latest revision as of 08:50, 12 February 2021


Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems
Authors
Antonis Loizou
Srinandan Dasmahapatra
Publication date
2010
DOI
10.1109/SMAP.2010.5706870
Links
Original

Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems - scientific work related to Wikipedia quality published in 2010, written by Antonis Loizou and Srinandan Dasmahapatra.

Overview

This paper proposes that Wikipedia can effectively be used in order to lessen the negative effects of data sparsity on the accuracy of recommendations produced by Recommender Systems, provided that domain resources available for recommendation can successfully be mapped to Wikipedia articles. Under the assumption that hyperlinks between Wikipedia articles convey latent semantic relationships between the concepts they represent, authors argue that by representing domain resources as a set of interconnected Wikipedia articles the volume of information available to a recommender algorithm increases, enabling it to improve its performance. The approach is evaluated using two real world datasets, giving positive results.

Embed

Wikipedia Quality

Loizou, Antonis; Dasmahapatra, Srinandan. (2010). "[[Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems]]".DOI: 10.1109/SMAP.2010.5706870.

English Wikipedia

{{cite journal |last1=Loizou |first1=Antonis |last2=Dasmahapatra |first2=Srinandan |title=Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems |date=2010 |doi=10.1109/SMAP.2010.5706870 |url=https://wikipediaquality.com/wiki/Using_Wikipedia_to_Alleviate_Data_Sparsity_Issues_in_Recommender_Systems}}

HTML

Loizou, Antonis; Dasmahapatra, Srinandan. (2010). &quot;<a href="https://wikipediaquality.com/wiki/Using_Wikipedia_to_Alleviate_Data_Sparsity_Issues_in_Recommender_Systems">Using Wikipedia to Alleviate Data Sparsity Issues in Recommender Systems</a>&quot;.DOI: 10.1109/SMAP.2010.5706870.