Please use this identifier to cite or link to this item:
http://hdl.handle.net/2067/46349
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Anelli, Vito Walter | it |
dc.contributor.author | Bellogín, Alejandro | it |
dc.contributor.author | Ferrara, Antonio | it |
dc.contributor.author | Malitesta, Daniele | it |
dc.contributor.author | Merra, Felice Antonio | it |
dc.contributor.author | Pomo, Claudio | it |
dc.contributor.author | Donini, Francesco Maria | it |
dc.contributor.author | Di Noia, Tommaso | it |
dc.date.accessioned | 2021-12-30T09:36:42Z | - |
dc.date.available | 2021-12-30T09:36:42Z | - |
dc.date.issued | 2021 | it |
dc.identifier.isbn | 9781450384582 | it |
dc.identifier.uri | http://hdl.handle.net/2067/46349 | - |
dc.description.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. | it |
dc.format.medium | ELETTRONICO | it |
dc.language.iso | eng | it |
dc.title | V-Elliot: Design, evaluate and tune visual recommender systems | it |
dc.type | conferenceObject | * |
dc.identifier.doi | 10.1145/3460231.3478881 | it |
dc.identifier.scopus | 2-s2.0-85115612503 | it |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85115612503 | it |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3460231.3478881 | it |
dc.relation.ispartofbook | RecSys 2021 - 15th ACM Conference on Recommender Systems | it |
dc.relation.firstpage | 768 | it |
dc.relation.lastpage | 771 | it |
dc.relation.numberofpages | 4 | it |
dc.relation.alleditors | Vito Walter Anelli, Pierpaolo Basile, Tommaso Di Noia, Francesco M Donini, Cataldo Musto, Fedelucio Narducci, Markus Zanker | it |
dc.relation.conferencename | RecSys 2021 - 15th ACM Conference on Recommender Systems | it |
dc.relation.conferenceplace | Amsterdam, Netherlands | it |
dc.relation.conferencedate | 27 September 2021 through 1 October 2021 | it |
dc.subject.scientificsector | ING-INF/05 | it |
dc.subject.scientificsector | INF/01 | it |
dc.description.numberofauthors | 8 | it |
dc.description.international | sì | it |
dc.contributor.country | ITA | it |
dc.contributor.country | ESP | it |
dc.type.referee | REF_1 | it |
dc.type.invited | no | it |
dc.type.miur | 273 | * |
dc.publisher.name | Association for Computing Machinery | it |
dc.publisher.place | New York NY | it |
dc.publisher.country | USA | it |
item.fulltext | With Fulltext | - |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | restricted | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | D1. Contributo in Atti di convegno |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RecSys2021___V_Elliot__Design__Evaluate_and_Tune_Visual_Recommender_Systems.pdf | Manuscript submitted to ACM | 760.23 kB | Adobe PDF | View/Open |
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