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Niu, "Defending shilling attacks in recommender systems using soft co-clustering," IET Information Security, vol.
The DealSmash Recommender System which is essentially the spam filter or modern marketer's funnel would help provide contextual awareness and personalized recommendations to retail customers.
To the best of our knowledge our work presented in this paper contributes to the existing body of knowledge in the academic and practical domain of Recommender system by proposing and empirically demonstrating for the first time the TL approach based on Basic Human Values, as a solution to alleviate the sparisty problem in a collaborative filtering based recommender system.
In this paper linguistic variables [14] are represented using single valued neutrosophic numbers [13] for developing knowledge based recommender system.
trust-aware recommender systems can help enable more items for recommendation while preserving competing predictive accuracy, where trust is propagated in trust networks to evaluate users' weights.
Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the MultiRadar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations.
Section 2 surveys some works related to private-preserving in recommender systems.
One of the most successful recommender system methods is based on the collaborative filtering technique [3-5].
The recommender systems are most popular intelligent software systems of the information filtering systems that applied in a various domains for example movies, music, books, jokes, restaurant, financial services [8], and Twitter followers [9].
The company was recognized for capabilities that include recommender engine, which helps companies drive employee engagement by leveraging machine-learning insights to uncover the most relevant digital workplace experiences and content.
Keywords: virtual assistants, language processing, cultural heritage, recommender systems, artificial intelligence
* Trust Solicitation: In this method trust "evaluating node" requests certain node (recommender node) to share its trust value regarding an "evaluated node".