Abstract
Recommender systems become increasingly significant in solving the information overload problem. Beyond conventional rating prediction and ranking prediction recommendation technologies, two-step recommendation algorithms have been demonstrated that they have outstanding accuracy performance in top-N recommendation tasks. However, their recommendation lists are biased towards popular items. In this paper, we propose a popularity normalization method to improve the diversity of user-based two-step recommendation algorithms. Experiment results show that our proposed approach improves the diversity performance significantly while maintaining the advantage of two-step recommendation approaches on accuracy metrics.
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Notes
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0 is a typical value out of the range of rating scale, which can be used to distinguish the rating value and the rating behavior.
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Types of implicit feedback include rating behaviors, purchase history, browsing history, and search patterns.
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Acknowledgements
This work is supported by the National Key Technology R&D Program of China (project no. 2014BAD10B08).
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Zhao, X., Chen, W., Yang, F., Liu, Z. (2016). Improving Diversity of User-Based Two-Step Recommendation Algorithm with Popularity Normalization. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_2
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