Properties of Context-Aware Recommender Systems: A Survey
Fateme Keikha, Mahdi Heidari
Abstract
Open-access reader
Fateme Keikha, Mahdi Heidari
Abstract
Open-access reader
Recommender systems provide personalized recommendation for their users.These systems are still needed to be optimized to provide more effective recommendations.In some models the context of user and the item is considered during the recommendation process so that it would be possible to make a better estimation of the user's rating.In this article context aware recommender models are addressed.Also the properties of each one of these systems are specified based on the general characteristics of the context aware recommender systems.Finally a general comparison of the level of utilization of these characteristics in the context aware models is done.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Recommender systems provide personalized recommendation for their users.These systems are still needed to be optimized to provide more effective recommendations.In some models the context of user and the item is considered during the recommendation process so that it would be possible to make a better estimation of the user's rating.In this article context aware recommender models are addressed.Also the properties of each one of these systems are specified based on the general characteristics of the context aware recommender systems.Finally a general comparison of the level of utilization of these characteristics in the context aware models is done.
Key concepts: Computer science, Recommender system, Context (archaeology), Information retrieval, Data science, World Wide Web, Paleontology, Biology