The Science of Online Data at Plenty of Fish with Thomas Levi
Data Skeptic5 Joulu 2014

The Science of Online Data at Plenty of Fish with Thomas Levi

Can algorithms help you find love? Many happy couples successfully brought together via online dating websites show us that data science can help you find love. I'm joined this week by Thomas Levi, Senior Data Scientist at Plenty of Fish, to discuss some of his work which helps people find one another as efficiently as possible.

Matchmaking is a truly non-trivial problem, and one that's dynamically changing all the time as new users join and leave the "pool of fish". This episode explores the aspects of what makes this a tough problem and some of the ways POF has been successfully using data science to solve it, and continues to try to innovate with new techniques like interest matching.

For his benevolent references, Thomas suggests readers check out All of Statistics as well as the caret library for R. And for a self serving recommendation, follow him on twitter (@tslevi) or connect with Thomas Levi on Linkedin.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(601)

Video Recommendations in Industry

Video Recommendations in Industry

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...

26 Joulu 202538min

Eye Tracking in Recommender Systems

Eye Tracking in Recommender Systems

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...

18 Joulu 202552min

Cracking the Cold Start Problem

Cracking the Cold Start Problem

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...

8 Joulu 202539min

Designing Recommender Systems for Digital Humanities

Designing Recommender Systems for Digital Humanities

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te...

23 Marras 202536min

DataRec Library for Reproducible in Recommend Systems

DataRec Library for Reproducible in Recommend Systems

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea...

13 Marras 202532min

Shilling Attacks on Recommender Systems

Shilling Attacks on Recommender Systems

In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm...

5 Marras 202534min

Music Playlist Recommendations

Music Playlist Recommendations

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste...

29 Loka 202552min

Bypassing the Popularity Bias

Bypassing the Popularity Bias

15 Loka 202534min

Suosittua kategoriassa Tiede

rss-mita-tulisi-tietaa
rss-poliisin-mieli
tiedekulma-podcast
menologeja-tutkimusmatka-vaihdevuosiin
rss-duodecim-lehti
docemilia
rss-astetta-parempi-elama-podcast
rss-lapsuuden-rakentajat-podcast
utelias-mieli
radio-antro
rss-ranskaa-raakana
rss-kasvatuspsykologiaa-kaikille
rss-tiedetta-vai-tarinaa
rss-sosiopodi
rss-totuuden-liepeilla