Name
Title | ||
|---|---|---|
How to put users in control of their data in federated top-N recommendation with learning to rank |
Abstract | ||
|---|---|---|
ABSTRACTRecommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. To address this issue, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning-to-rank optimization by following the Federated Learning principles, originally conceived to mitigate the privacy risks of traditional machine learning. The public implementation is available at https://split.to/sisinflab-fpl. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3412841.3442010 | Symposium on Applied Computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Walter Anelli | 1 | 91 | 18.45 |
| Yashar Deldjoo | 2 | 186 | 24.74 |
| Tommaso Di Noia | 3 | 1857 | 152.07 |
| Antonio Ferrara | 4 | 18 | 3.94 |
| Fedelucio Narducci | 5 | 160 | 24.77 |