Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

@article{Shi2014CollaborativeFB,
  title={Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges},
  author={Yue Shi and M. Larson and A. Hanjalic},
  journal={ACM Comput. Surv.},
  year={2014},
  volume={47},
  pages={3:1-3:45}
}
  • Yue Shi, M. Larson, A. Hanjalic
  • Published 2014
  • Computer Science
  • ACM Comput. Surv.
  • Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 18 REFERENCES
    Item-based collaborative filtering recommendation algorithms
    • 7,052
    • Highly Influential
    • PDF
    Collaborative Filtering Recommender Systems
    • 570
    • Highly Influential
    • PDF
    Improving one-class collaborative filtering by incorporating rich user information
    • 112
    • Highly Influential
    • PDF
    Transfer learning for collaborative filtering via a rating-matrix generative model
    • 270
    • Highly Influential
    • PDF
    Amazon.com Recommendations: Item-to-Item Collaborative Filtering
    • 4,783
    • Highly Influential
    • PDF
    Transfer Learning in Collaborative Filtering for Sparsity Reduction
    • 266
    • Highly Influential
    • PDF
    Empirical Analysis of Predictive Algorithms for Collaborative Filtering
    • 5,116
    • Highly Influential
    • PDF
    The task-dependent effect of tags and ratings on social media access
    • 39
    • Highly Influential
    • PDF
    Handling data sparsity in collaborative filtering using emotion and semantic based features
    • 89
    • Highly Influential
    Evaluating collaborative filtering recommender systems
    • 5,135
    • Highly Influential
    • PDF