An Adjusted Recommendation List Size Approach for Users' Multiple Item Preferences

Date

2016-09-10

Author

Peker, Serhat
Koçyiğit, Altan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper describes the design and implementation of a novel approach to dynamically adjust the recommendation list size for multiple preferences of a user. By considering users' earlier preferences, machine learning techniques are employed to estimate the optimal recommendation list size according to current conditions of users. The proposed approach has been evaluated on real-life data from grocery shopping domain by conducting a series of experiments. The results show that the proposed approach achieves better overall recommendation quality than the standard approach and it outperforms the benchmark method in efficiency by shortening the recommendation list while maintaining the effectiveness.

Subject Keywords

Top-N recommender systems, Recommendation list size, Recommendation length, Recommendation quality, Recommendation efficiency

URI

https://hdl.handle.net/11511/31370

DOI

https://doi.org/10.1007/978-3-319-44748-3_30

Conference Name

17th International Conference on Artificial Intelligence - Methodology, Systems and Applications (AIMSA)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

S. Peker and A. Koçyiğit, “An Adjusted Recommendation List Size Approach for Users’ Multiple Item Preferences,” Varna, BULGARIA, 2016, vol. 9883, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31370.