V-Elliot: Design, evaluate and tune visual recommender systems
Author(s)
Date Issued
2021
Type
conferenceObject
Start Page
768
End Page
771
Abstract
The paper introduces Visual-Elliot (V-Elliot), a reproducibility framework for Visual Recommendation systems (VRSs) based on Elliot. framework provides the widest set of VRSs compared to other recommendation frameworks in the literature (i.e., 6 state-of-the-art models which have been commonly employed as baselines in recent works). The framework pipeline spans from the dataset preprocessing and item visual features loading to easily train and test complex combinations of visual models and evaluation settings. V-Elliot provides an extended set of features to ease the design, testing, and integration of novel VRSs into V-Elliot. The framework exploits of dataset filtering/splitting functions, 40 evaluation metrics, five hyper-parameter optimization methods, more than 50 recommendation algorithms, and two statistical hypothesis tests. The files of this demonstration are available at: github.com/sisinflab/elliot.
File(s)![Thumbnail Image]()
Name
RecSys2021___V_Elliot__Design__Evaluate_and_Tune_Visual_Recommender_Systems.pdf
Description
Manuscript submitted to ACM
Size
760.23 KB
Format
Adobe PDF
Checksum (MD5)
063b519dea28dd30bfc2574e9ef7fc58
Conference(s)
RecSys 2021 - 15th ACM Conference on Recommender Systems