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A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
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TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Recsplorer: recommendation algorithms based on precedence mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Soft-constraint based online LDA for community recommendation
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Recommender Systems Handbook
Assessing and ranking structural correlations in graphs
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Proceedings of the 28th Annual ACM Symposium on Applied Computing
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 7th ACM conference on Recommender systems
Exploiting non-content preference attributes through hybrid recommendation method
Proceedings of the 7th ACM conference on Recommender systems
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference
Proceedings of the 7th ACM international conference on Web search and data mining
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The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been largely attributed to a "long tail" phenomenon. Although the majority of their inventory is not in high demand, these niche products, unavailable at limited-inventory competitors, generate a significant fraction of total revenue in aggregate. In addition, tail product availability can boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche tastes. However, most of existing recommender systems, especially collaborative filter based methods, can not recommend tail products due to the data sparsity issue. It has been widely acknowledged that to recommend popular products is easier yet more trivial while to recommend long tail products adds more novelty yet it is also a more challenging task. In this paper, we propose a novel suite of graph-based algorithms for the long tail recommendation. We first represent user-item information with undirected edge-weighted graph and investigate the theoretical foundation of applying Hitting Time algorithm for long tail item recommendation. To improve recommendation diversity and accuracy, we extend Hitting Time and propose efficient Absorbing Time algorithm to help users find their favorite long tail items. Finally, we refine the Absorbing Time algorithm and propose two entropy-biased Absorbing Cost algorithms to distinguish the variation on different user-item rating pairs, which further enhances the effectiveness of long tail recommendation. Empirical experiments on two real life datasets show that our proposed algorithms are effective to recommend long tail items and outperform state-of-the-art recommendation techniques.