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This paper proposes a social matching system that uses past relations and user similarities in finding potential matches.The proposed system is evaluated on the dataset collected from an online dating network.Empirical analysis shows that the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.Tesh recognised the potential of Tallysticks after working as a Machine Learning Consultant and Product Manager for KPMG London applying ML solutions to Financial Services.

She is an experienced data and semantic modeller and has been working with XBRL for over 15 years.Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days.Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual relationship.Most recently, Donna worked in wealth management as a financial planner for high net worth clientele.Donna founded Rate Seer in 2015 to gather financial and banking data in low cost ways to support informed financial decisions that create better lives.

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