RecSys ’19: Pace My Race: Recommendations for Marathon Running
Recommendation system paper challenge (18/50)
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 ’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/
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