Proceedings ArticleDOI
Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems
Marcos Aurélio Domingues,Marcelo G. Manzato,Ricardo Marcondes Marcacini,Camila Vaccari Sundermann,Solange Oliveira Rezende +4 more
- pp 3606-3611
TLDR
This paper proposes to use contextual information from topic hierarchies to improve the accuracy of context-aware recommender systems and proposes two context- aware recommender algorithms for item recommendation.Abstract:
Unlike the traditional recommender systems, that make recommendations only by using the relation between user and item, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process as explicit additional categories of data to improve the recommendation process. In this paper, we propose to use contextual information from topic hierarchies to improve the accuracy of context-aware recommender systems. Additionally, we also propose two context-aware recommender algorithms for item recommendation. These are extensions from algorithms proposed in literature for rating prediction. The empirical results demonstrate that by using topic hierarchies our technique can provide better recommendations.read more
Citations
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Journal ArticleDOI
Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
Camila Vaccari Sundermann,Marcos Aurélio Domingues,Roberta Akemi Sinoara,Ricardo Marcondes Marcacini,Solange Oliveira Rezende +4 more
TL;DR: A systematic review on the recommender systems that explore both contextual information and opinion mining, based on 17 papers selected among 195 papers identified in four digital libraries.
Journal ArticleDOI
Multi-View Fuzzy Information Fusion in Collaborative Filtering Recommender Systems: Application to the Urban Resilience Domain
TL;DR: A hybrid framework which combines a collaborative filtering recommendation system with fuzzy decision-making approaches (based on the use of aggregation functions) to improve the accuracy of domain-specific recommendations is proposed.
Journal ArticleDOI
Experimental Validation of Contextual Variables for Research Resources Recommender System
TL;DR: This paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource into three major sections (introduction, review and methodology), showing that these three variables could be used as context.
Proceedings ArticleDOI
Context-Aware Recommender System Frameworks, Techniques, and Applications: A Survey
TL;DR: A survey of Context Aware Recommendation Systems overview, frameworks, techniques, algorithms, and applications is presented and the issues of CARSs in general are discussed and the proposed solutions are investigated.
Proceedings ArticleDOI
Contextual-Aware Hybrid Recommender System for Mixed Cold-Start Problems in Privacy Protection
TL;DR: A Weighted Switching Hybrid Context-Aware (W-SHCA) Recommender System based on two algorithms, which utilizes their weighted sum to perform the prediction when a mixed cold start problem is detected and deduced a stable weight selection pattern for W-SH CA associating with dataset characteristics.
References
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Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Understanding and Using Context
TL;DR: An operational definition of context is provided and the different ways in which context can be used by context-aware applications are discussed, including the features and abstractions in the toolkit that make the task of building applications easier.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
BookDOI
Recommender Systems Handbook
TL;DR: This handbook illustrates how recommender systems can support the user in decision-making, planning and purchasing processes, and works for well known corporations such as Amazon, Google, Microsoft and AT&T.