2 research outputs found
Web3Recommend: Decentralised recommendations with trust and relevance
Web3Recommend is a decentralized Social Recommender System implementation
that enables Web3 Platforms on Android to generate recommendations that balance
trust and relevance. Generating recommendations in decentralized networks is a
non-trivial problem because these networks lack a global perspective due to the
absence of a central authority. Further, decentralized networks are prone to
Sybil Attacks in which a single malicious user can generate multiple fake or
Sybil identities. Web3Recommend relies on a novel graph-based content
recommendation design inspired by GraphJet, a recommendation system used in
Twitter enhanced with MeritRank, a decentralized reputation scheme that
provides Sybil-resistance to the system. By adding MeritRank's decay parameters
to the vanilla Social Recommender Systems' personalized SALSA graph algorithm,
we can provide theoretical guarantees against Sybil Attacks in the generated
recommendations. Similar to GraphJet, we focus on generating real-time
recommendations by only acting on recent interactions in the social network,
allowing us to cater temporally contextual recommendations while keeping a
tight bound on the memory usage in resource-constrained devices, allowing for a
seamless user experience. As a proof-of-concept, we integrate our system with
MusicDAO, an open-source Web3 music-sharing platform, to generate personalized,
real-time recommendations. Thus, we provide the first Sybil-resistant Social
Recommender System, allowing real-time recommendations beyond classic
user-based collaborative filtering. The system is also rigorously tested with
extensive unit and integration tests. Further, our experiments demonstrate the
trust-relevance balance of recommendations against multiple adversarial
strategies in a test network generated using data from real music platforms
Web3Recommend: Decentralised recommendations with trust and relevance
Web3Recommend is a decentralized Social Recommender System implementation that enables Web3 Platforms on Android to generate recommendations that balance trust and relevance. Generating recommendations in decentralized networks is a non-trivial problem because these networks lack a global perspective due to the absence of a central authority. Further, decentralized networks are prone to Sybil Attacks in which a single malicious user can generate multiple fake or "Sybil" identities. Web3Recommend relies on a novel graph-based content recommendation design inspired by GraphJet, a recommendation system used in Twitter enhanced with MeritRank, a decentralized reputation scheme that provides Sybil-resistance to the system. By adding MeritRank's decay parameters to the vanilla Social Recommender Systems’ personalized SALSA graph algorithm, we can provide theoretical guarantees against Sybil Attacks in the generated recommendations. Similar to GraphJet, we focus on generating real-time recommendations by only acting on recent interactions in the social network, allowing us to cater temporally contextual recommendations while keeping a tight bound on the memory usage in resource-constrained devices, allowing for a seamless user experience. As a proof-of-concept, we integrate our system with MusicDAO, an open-source Web3 music-sharing platform, to generate personalized, real-time recommendations. Thus, we provide the first Sybil-resistant Social Recommender System, allowing real-time recommendations beyond classic user-based collaborative filtering. The system is also rigorously tested with extensive unit and integration tests. Further, our experiments demonstrate the trust-relevance balance of recommendations against multiple adversarial strategies in a test network generated using data from real music platforms.Computer Science | Data Science and Technolog