Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
- URL: http://arxiv.org/abs/2202.07253v1
- Date: Tue, 15 Feb 2022 08:46:34 GMT
- Title: Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
- Authors: Jamie Cui and Chaochao Chen and Lingjuan Lyu and Carl Yang and Li Wang
- Abstract summary: Social recommendation has shown promising improvements over traditional systems.
Most existing work assumes that all data are available to the recommendation platform.
We propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework.
- Score: 34.60672247558132
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social recommendation has shown promising improvements over traditional
systems since it leverages social correlation data as an additional input. Most
existing work assumes that all data are available to the recommendation
platform. However, in practice, user-item interaction data (e.g.,rating) and
user-user social data are usually generated by different platforms, and both of
which contain sensitive information. Therefore, "How to perform secure and
efficient social recommendation across different platforms, where the data are
highly-sparse in nature" remains an important challenge. In this work, we bring
secure computation techniques into social recommendation, and propose S3Rec, a
sparsity-aware secure cross-platform social recommendation framework. As a
result, our model can not only improve the recommendation performance of the
rating platform by incorporating the sparse social data on the social platform,
but also protect data privacy of both platforms. Moreover, to further improve
model training efficiency, we propose two secure sparse matrix multiplication
protocols based on homomorphic encryption and private information retrieval.
Our experiments on two benchmark datasets demonstrate the effectiveness of
S3Rec.
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