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Step Out of Your Comfort Zone: More Inclusive Content Recommendation for Networked Systems
[article]
2021
arXiv
pre-print
Networked systems are widely applicable in real-world scenarios such as social networks, infrastructure networks, and biological networks. Among those applications, we are interested in social networks due to their complexity and popularity. One crucial task on the social network is to recommend new content based on special characteristics of the graph structure. In this project, we aim to enhance the recommender systems by preventing the recommendations from leaning towards contents from
arXiv:2106.10408v1
fatcat:tccfan6a6zgmthfciwg2q3lrmm