Corpus ID: 18540015

A Survey Paper on Recommender Systems

@article{Almazro2010ASP,
  title={A Survey Paper on Recommender Systems},
  author={Dhoha Almazro and Ghadeer Shahatah and Lamia Albdulkarim and Mona Kherees and Romy Martinez and William Nzoukou},
  journal={ArXiv},
  year={2010},
  volume={abs/1006.5278}
}
Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. [...] Key Result We conclude by proposing our approach that might enhance the quality of recommender systems.Expand
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A unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items is proposed that outperforms the state of the art recommender system algorithms, eliminates recorded problems with recommender systems. Expand
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This article presents one class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended, and shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality. Expand
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An agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests is proposed and empirical evaluation demonstrates that the interaction among personal agents increases the performance of the overallRecommender system. Expand
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