Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering
1
Department of Digital Systems, University of the Peloponnese, Valioti’s Building, Kladas, 231 00 Sparti, Greece
2
Department of Management Science and Technology, University of the Peloponnese, Akadimaikou G. K. Vlachou, 221 31 Tripoli, Greece
3
Department of Informatics and Telecommunications, University of the Peloponnese, Akadimaikou G. K. Vlachou, 221 31 Tripoli, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Ángel González-Prieto
Appl. Sci. 2021, 11(18), 8369; https://doi.org/10.3390/app11188369
Received: 2 August 2021
/
Revised: 7 September 2021
/
Accepted: 7 September 2021
/
Published: 9 September 2021
(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)
In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.
View Full-Text
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Margaris, D.; Spiliotopoulos, D.; Vassilakis, C. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Appl. Sci. 2021, 11, 8369. https://doi.org/10.3390/app11188369
AMA Style
Margaris D, Spiliotopoulos D, Vassilakis C. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Applied Sciences. 2021; 11(18):8369. https://doi.org/10.3390/app11188369
Chicago/Turabian StyleMargaris, Dionisis, Dimitris Spiliotopoulos, and Costas Vassilakis. 2021. "Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering" Applied Sciences 11, no. 18: 8369. https://doi.org/10.3390/app11188369
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
