Journal ArticleDOI
Trust and Distrust based Cross-domain Recommender System
Richa,Punam Bedi +1 more
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TLDR
In this article, a recommender system (RS) provides assistance for users to filter out items of their interest in the presence of millions of available items, the reason is to find out the similarly user with the same interest.Abstract:
A recommender system (RS) provides assistance for users to filter out items of their interest in the presence of millions of available items. The reason is to find out the likewise user with the as...read more
Citations
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Journal ArticleDOI
An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering
Lei Fu,XiaoMing Ma +1 more
TL;DR: Wang et al. as discussed by the authors proposed an online marketing recommendation algorithm based on the integration of content and collaborative filtering, which can effectively retain customers, prevent customer loss and increase the cross-selling volume of the e-commerce system.
Journal ArticleDOI
High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach
TL;DR: In this article , the authors proposed RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach to address these uncertain systems for the recommendation of quality research papers, which makes use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering.
Journal ArticleDOI
Personality-based and trust-aware products recommendation in social networks
A Two-Step Best-Worst Method (BWM) and K-Means Clustering Recommender System Framework
TL;DR: In this article, the authors suggested that the clusters of multi-criteria decision-making (MCDM) weights can be used as a representation for the diversity of priorities in society.
Journal ArticleDOI
Handling Cold-Start Problem in Restaurant Recommender System Using Ontology
TL;DR: In this article , an ontology-based recommendation system is proposed to provide restaurant recommendations based on user preferences such as location, cuisine, etc. The user will be able to input the desired preferences and the appropriate recommendation, i.e., the restaurant name, will be fetched.
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.
Journal ArticleDOI
Recommender systems
Paul Resnick,Hal R. Varian +1 more
TL;DR: This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
Journal ArticleDOI
A survey of trust and reputation systems for online service provision
TL;DR: Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision as mentioned in this paper, where the basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score.
Proceedings ArticleDOI
Social information filtering: algorithms for automating “word of mouth”
Upendra Shardanand,Pattie Maes +1 more
TL;DR: The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.