12,175 research outputs found
User recommendation algorithm in social tagging system based on hybrid user trust
With the rapid growth of web 2.0 technologies, tagging become much more important today to facilitate personal organization and also provide a possibility for users to search information or discover new things with Collaborative Tagging Systems. However, the simplistic and user-centered design of this kind of systems cause the task of finding personally interesting users is becoming quite out of reach for the common user. Collaborative Filtering (CF) seems to be the most popular technique in recommender systems to deal with information overload issue but CF suffers from accuracy limitation. This is because CF always been attack by malicious users that will make it suffers in finding the truly interesting users. With this problem in mind, this study proposes a hybrid User Trust method to enhance CF in order to increase accuracy of user
recommendation in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is derived from personalized user tagging information. The hybrid User Trust method is able to find the most trusted users and selected as neighbours to generate recommendations. Experimental results show that the hybrid method outperforms the traditional CF algorithm. In addition, it indicated that the hybrid method give more accurate recommendation than the existing CF based on user trust
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
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Information Filtering on Coupled Social Networks
In this paper, based on the coupled social networks (CSN), we propose a
hybrid algorithm to nonlinearly integrate both social and behavior information
of online users. Filtering algorithm based on the coupled social networks,
which considers the effects of both social influence and personalized
preference. Experimental results on two real datasets, \emph{Epinions} and
\emph{Friendfeed}, show that hybrid pattern can not only provide more accurate
recommendations, but also can enlarge the recommendation coverage while
adopting global metric. Further empirical analyses demonstrate that the mutual
reinforcement and rich-club phenomenon can also be found in coupled social
networks where the identical individuals occupy the core position of the online
system. This work may shed some light on the in-depth understanding structure
and function of coupled social networks
A Distributed Method for Trust-Aware Recommendation in Social Networks
This paper contains the details of a distributed trust-aware recommendation
system. Trust-base recommenders have received a lot of attention recently. The
main aim of trust-based recommendation is to deal the problems in traditional
Collaborative Filtering recommenders. These problems include cold start users,
vulnerability to attacks, etc.. Our proposed method is a distributed approach
and can be easily deployed on social networks or real life networks such as
sensor networks or peer to peer networks
Trust networks for recommender systems
Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.)
We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators.
In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections
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