Comparison of Pre and Post-Filtering Algorithms for Conditional Recommendation

Authors: ,

Abstract: The demand for recommendation systems gets higher and more popular these days. There have been several studies on recommendation systems, but the most recent ones give focus on the product as a whole and do not give much attention to the user’s preferences such as price, type, and color. This paper proposes pre-filtering methods and compares the benefits and performance between pre- and post-filtering methods. The pre-filtering method ignores ratings of items that are not relevant to the user’s preferences, then reduces the size of target data set to process, saving processing time. The experimental result with MovieLens dataset shows that pre-filtering can provide the recommendation with 8.5 times less computations than post-filtering by restricting item set, and shows 2% improvement in F measurement. Moreover, rating estimation performance can vary from 1% improvement in the ML-1M dataset to 1% decrease in the ML-100K dataset in the RMSE.

WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 14, 2017, Art. #36

PDF
XML
Cite
×
Citation Tools
WSEAS AMA
Giang Nguyen Trung, Heejune Ahn, "Comparison of Pre and Post-Filtering Algorithms for Conditional Recommendation," WSEAS Transactions on Computers, vol. 14, pp. 314-319, 2017, DOI:
Giang Nguyen Trung, Heejune Ahn. Comparison of Pre and Post-Filtering Algorithms for Conditional Recommendation. WSEAS Transactions on Computers. 2017;14:314-319.
Copy to Clipboard
Citation copied to Clipboard

Certification

---