DEIM2021技術報告 / 会社訪問アプリ「Wantedly Visit」のデータで見る相互推薦システム

Mar 2021

Weighted Averaging of Various LSTM Models for Next Destination Recommendation

This paper describes the 6th place approach to Booking.com WSDM WebTour 2021 Challenge, which is a challenge with a task of predicting travellers’ next destination. We, in the team "hakubishin3 & u++ & yu-y4", trained four types of Long short-term memory (LSTM) models, and achieved the final score: 0.5399 by weighted averaging of these predictions. There are some differences in these models in feature engineering, multi-task learning, and data augmentation. Our experiments showed that the diversity of the models boosted the final result. Our codes are available at https://github. com/hakubishin3/booking-challenge-2021 and https://github.com/ upura/booking-challenge-2021.

Mar 2021

Wantedly RecSys 2020 参加レポート⑥ - Offline Evaluation in Recommender Systems

Oct 2020

RecSys Challenge 2020 Workshop: A Stacking Ensemble Model for Prediction of Multi-type Tweet Engagements

Sept 2020

A Stacking Ensemble Model for Prediction of Multi-type Tweet Engagements

The RecSys Challenge 2020 is a competition with a task of predicting four types of user engagements on Twitter: Like, Reply, Retweet and Retweet with comment. In this paper, we describe Team Wantedly’s approach to this challenge, which won the third place. We found that the targets are highly correlated and it is important to use every engagement to predict the other engagements. Therefore, we choose to stack LightGBM models to use this co-occurrences effectively in the large dataset. Our final scores are as follows: 1.5266 (Retweet PR-AUC), 30.06 (Retweet RCE), 0.1918 (Reply PR-AUC), 20.44 (Reply RCE), 0.7716 (Like PR-AUC), 24.76 (Like RCE), 0.0724 (Retweet with comment PR-AUC), 14.86 (Reply RCE). Our code is available at https://github.com/wantedly/recsys2020-challenge. Shuhei Goda, Naomichi Agata, and Yuya Matsumura. 2020. A Stacking Ensemble Model for Prediction of Multi-Type Tweet Engagements. In Proceedings of the Recommender Systems Challenge 2020. 6-10.

Sept 2020

Kaggle Google Quest Q&A Labeling - 23rd place solution

Feb 2020

Kaggle Days Tokyo Report #2

Dec 2019

Target Encoding はなぜ有効なのか

Nov 2019

Kaggle Masterになるまでを振り返る

Oct 2019

The Web Conference2020 参加報告会 - 論文・セッション紹介

Apr 2020