Collaborative Filtering Ensemble for Personalized Name Recommendation

TL;DR

This paper presents an ensemble collaborative filtering method to generate personalized baby name recommendations, leveraging real-world query log data for improved accuracy and efficiency.

Contribution

It introduces a simple, fast ensemble approach combining collaborative filtering algorithms for personalized name recommendation, validated on real-world data from nameling.net.

Findings

Effective personalization of name suggestions based on user preferences.

Fast training and prediction times for practical deployment.

Improved recommendation accuracy over individual algorithms.

Abstract

Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follow a recommender system approach and combine, in an ensemble, the individual rankings produced by simple collaborative filtering algorithms in order to produce a personalized list of names that meets the individual parents' taste. Our experiments were conducted using real-world data collected from the query logs of 'nameling' (nameling.net), an online portal for searching and exploring names, which corresponds to the dataset released in the context of the ECML PKDD Discover Challenge 2013. Our approach is intuitive, easy to implement, and features fast training and prediction steps.