RecSys ’19: Pace My Race: Recommendations for Marathon Running

Recommendation system paper challenge (18/50)

paper link

What problem do they solve?

They want to predict the finish time for Marathon.

Besides that, they want to recommend the pacing plan to the runner when to run slowly down to safely finish the marathon.

What is the model they propose?

For predicting finish time, they applied XGBM model with HR, pace, Cadence features and extend them with time series model

For recommending the pacing plan: applying the following steps

Features

Result with time serious extended or not

Result between the model and baseline model

Baseline model: where MPR is the mean pace of the runner to that point

Example Product

Other related blogs:

Beyond Clicks: Dwell Time for Personalization

RecSys16: Adaptive, Personalized Diversity for Visual Discovery

NAACL’19: Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

RecSys ’17: Translation-based Recommendation

RecSys ’17: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

RecSys ’18: HOP-Rec: High-Order Proximity for Implicit Recommendation

RecSys ’18: Impact of item consumption on assessment of recommendations in user studies

RecSys ’18: Generation Meets Recommendation: Proposing Novel Items for Groups of Users

RecSys ’18: Causal Embeddings for Recommendation

Best paper in RecSys:

https://recsys.acm.org/best-papers/

My Website:

https://light0617.github.io/#/

An machine learning engineer in Bay Area in the United States

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Arthur Lee

Arthur Lee

An machine learning engineer in Bay Area in the United States

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