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Context-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only consider the influence of con-textual information on item bias. Tensor factorization based Multiverse(More)
Trust-aware recommender system (TARS) can provide more relevant recommendation and more accurate rating predictions than the traditional recommender system by taking the trust network into consideration. However, most of the trust-aware collaborative filtering approaches do not consider the influence of contextual information on rating prediction. To the(More)
Lineage tracing queries help to locate updated views quickly in data warehouse. Materialized views can improve the efficiency of the data lineage tracing and view maintenance. This paper, a method to select materialized views using Top-k query algorithm is presented. The selection is based on the query frequency, the view storage space and maintenance cost.(More)
This paper proposes a new algorithm FIM_AIUA, which updates association rules with incremental transactions and minimum support changes simultaneously. The algorithm expands FIM algorithm and AIUA algorithm, improves the efficiency and corrects the mistakes of My_IUA algorithm. Moreover, it modifies FIM algorithm with a new argument and presents a new(More)
Trust-aware recommender system can provide more accurate rating predictions than traditional recommender system by taking the trust relationships between users into consideration. Yet the state-of-the-art improved trust-aware collaborative filtering approach only considers the user-based implicit trust network model and the influence of trust information on(More)
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