LibRec 精选：推荐系统9个必备数据集

新年开工大吉！本周的精选内容如下，请特别关注加粗的内容。

研究推荐系统的9个必备数据集

电影 (MovieLens)：http://grouplens.org/datasets/movielens/

笑话 (Jester)：http://eigentaste.berkeley.edu/dataset/

图书 (Book-Crossing)：http://www2.informatik.uni-freiburg.de/~cziegler/BX/

音乐 (Last.fm)：http://grouplens.org/datasets/hetrec-2011/

百科 (Wikipedia)：https://en.wikipedia.org/wiki/Wikipedia:Database_download#English-language_Wikipedia

地图 (OpenStreetMap)：http://planet.openstreetmap.org/planet/full-history/

开源 (Python Git Repositories)：操作源码见 https://github.com/lab41/hermes

编者附---竞赛 (Kaggle)：https://www.kaggle.com/kaggle/meta-kaggle

源自：https://gab41.lab41.org/the-nine-must-have-datasets-for-investigating-recommender-systems-ce9421bf981c

应用：

YouTube: Most recommended videos；https://t.co/sstFKLrUEb

Email attachments recommendation from RecSys

论文：

Gilotte et al., "offline A/B testingfor recommender systems", https://arxiv.org/abs/1801.07030，WSDM 2018

Quadrana et al., "Sequence-aware Recommender Systems", https://mquad.github.io/static/papers/2018-seqrec_survey.pdf

Trattner & Elsweiler, "Food Recommender Systems: Important Contributions, Challenges and Future Research Directions", https://www.researchgate.net/publication/320944468_Food_Recommender_Systems_Important_Contributions_Challenges_and_Future_Research_Directions

Trattner et al., "Investigating the utility of theweather context for point of interestrecommendations", https://link.springer.com/article/10.1007/s40558-017-0100-9

观点：

源自：https://medium.com/the-graph/insights-from-an-evening-with-recommender-systems-experts-ab44d677dc5e

RMSE is never an appropriate evluation metric

In most casesImplicit feedback is far more valuablethan explicit feedback

Ratingsthat are not observedare not missing at random

视频：

A LinearReinforcement LearningAlgorithm for Non Stationary Actions：https://www.youtube.com/watch?v=HUabXYYWHYs

Recurrent Neural Network for Session-based Recommendations：https://www.youtube.com/watch?v=M7FqgXySKYk