A Method Combined with User’s Scores for Optimizing

Association Rules

DOI

10.2991/978-94-6463-038-1_17How to use a DOI?

Keywords

association rules; user’s Scores; optimization method

Abstract

In personalized recommendation services, association rules are often used to provide users with appropriate recommendations. However, it is often difficult to choose the results of the optimal rule. Association rule recommendation methods, usually only need to make use of the transaction data set, and do not use many more accessible domain knowledge, the recommendation results are not satisfactory. This paper combines the collaborative filtering thinking and uses the user score information to propose a new rule result selection method, which combines the rules with consistent recommendation results, and then determines the similarity between the user and the combined rules by using user’s Scores, and then obtains the optimal recommendation result. Experimental results show that the proposed method has better user satisfaction compared to traditional methods.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qinglie Wu
AU  - Yu Yang
PY  - 2022
DA  - 2022/12/15
TI  - A Method Combined with User’s Scores for Optimizing
BT  - Proceedings of the 2022 3rd International Conference on Management Science and Engineering Management (ICMSEM 2022)
PB  - Atlantis Press
SP  - 160
EP  - 172
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-038-1_17
DO  - 10.2991/978-94-6463-038-1_17
ID  - Wu2022
ER  -