Title
Towards a knowledge-based probabilistic and context-aware social recommender system
Abstract
AbstractIn this article, we propose 1 a knowledge-based probabilistic collaborative filtering CF recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation LDA model-based recommendation technique and 2 a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain foodservice places. The ontology on which the similarity metric is based is additionally leveraged to model and reason about users' contexts; the proposed LDA model also guides the users' context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval IR metrics and a reference ranking-based evaluation metric correctly ranked places is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user's perspective. Our recommendation approach achieves higher average precision and recall values 8% and 7.40%, respectively in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users' preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places 2.5 of 5 versus 1.5 of 5.
Year
DOI
Venue
2018
10.1177/0165551517698787
Periodicals
Keywords
Field
DocType
Collaborative filtering,knowledge-based recommender systems,latent Dirichlet allocation,ontologies,Semantic Web
Recommender system,Ontology (information science),Semantic similarity,Data mining,Ontology,Latent Dirichlet allocation,Collaborative filtering,Information retrieval,Ranking,Computer science,Probabilistic logic
Journal
Volume
Issue
ISSN
44
4
0165-5515
Citations 
PageRank 
References 
4
0.38
30
Authors
5