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Within a set of search results, what should you do to find the optimal solution for not just a single user but a group? Mangaki is building an open source library for privacy-preserving group recommendations of items. While many content providers suggest recommendations at a personal level, these are often directed to a single user, or are restricted to a generic “family” category. Whenever say a group of friends want to watch a movie, it is often hard to decide what to watch, because people can have really different tastes.

Recommendations are also very privacy-sensitive. A straightforward way might be to share our complete viewing history, but that certainly can lead to embarrassing and awkward situations. So how can we collectively compute a list of relevant items without disclose all of our data unencrypted. The Mangaki project is making an open source library for group recommendations that works in a scalable and distributed way.

Run by Mangaki/Inria

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This project was funded through the NGI0 Discovery Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, under the aegis of DG Communications Networks, Content and Technology under grant agreement No 825322. Applications are still open, you can apply today.

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Please check out NLnet's theme funds, such as NGI Assure, NGI0 Discovery (which is focussed on search, discovery and discoverability) and the Internet Hardening Fund.

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