A Semantically Aware Explainable Recommender System using Asymmetric Matrix Factorization.
Mohammad Alshammari,Olfa Nasraoui,Behnoush Abdollahi +2 more
- pp 266-271
About:
This article is published in International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.The article was published on 2018-01-01 and is currently open access. It has received 4 citations till now. The article focuses on the topics: Matrix decomposition & Recommender system.read more
Citations
More filters
Journal ArticleDOI
Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems
TL;DR: This work proposes a novel approach to build an explanation generation mechanism into a latent factor-based black box recommendation model that is trained to learn to make predictions that are accompanied by explanations that are automatically mined from the semantic web.
Proceedings ArticleDOI
Towards Explanations of Anti-Recommender Content in Public Radio
TL;DR: An approach is proposed for designing explanations of recommendations that align with the public service remit in public radio that may be unexpected for users and may need explanation.
Proceedings ArticleDOI
A Restaurant Recommendation Engine Using Feature-based Explainable Matrix Factorization
TL;DR: In this article , the authors proposed a new approach for producing justifications while maintaining high accuracy and transparency in recommender systems, and demonstrated that their suggested solution performs better in terms of accuracy.
Proceedings ArticleDOI
A Restaurant Recommendation Engine Using Feature-based Explainable Matrix Factorization
TL;DR: In this article , the authors proposed a new approach for producing justifications while maintaining high accuracy and transparency in recommender systems, and demonstrated that their suggested solution performs better in terms of accuracy.
References
More filters
Journal ArticleDOI
WordNet: a lexical database for English
TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
TL;DR: The Explicit Factor Model (EFM) is proposed to generate explainable recommendations, meanwhile keep a high prediction accuracy, and online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.
Proceedings ArticleDOI
Tagsplanations: explaining recommendations using tags
Jesse Vig,Shilad Sen,John Riedl +2 more
TL;DR: This paper develops novel algorithms for estimating tag relevance and tag preference, and conducts a user study exploring the roles of tag relevanceand tag preference in promoting effective tagsplanations.
Proceedings Article
Explaining Recommendations: Satisfaction vs. Promotion
Mustafa Bilgic,Raymond J. Mooney +1 more
TL;DR: This work presents two new methods for explaining recommendations of contentbased and/or collaborative systems and experimentally shows that they actually improve user’s estimation of item quality.