Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Building user profiles to improve user experience in recommender systems
Proceedings of the sixth ACM international conference on Web search and data mining
The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems
IEEE Transactions on Knowledge and Data Engineering
User Modeling and User-Adapted Interaction
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Recommender systems are useful tools that help people to filter and explore massive information. While the accuracy of recommender systems is important, many recent research indicated that focusing merely on accuracy not only is insufficient to meet user needs, but also may be harmful. Other characteristics such as novelty, unexpectedness and diversity should also be taken into consideration. Previous work has shown that more the sales of long-tail items could be more beneficial to both customers and some business models. However, the majority of collaborative filtering approaches tends to recommend popular selling items. In this work, we focus on long-tail item promotion and aggregate diversity enhancement, and propose a novel approach which diversifies the results of recommender systems by considering ``recommendations" as resources to be allocated to the items. Our approach increases the quantity and quality of long-tail item recommendations by adding more variation into the recommendation and maintains a certain level of accuracy simultaneously. The experimental results show that this approach can discover more worth-recommending items from Long Tails and improves user experience.