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 testing for 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 the weather context for point of interest recommendations", 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 cases Implicit feedback is far more valuable than explicit feedback
Ratings that are not observed are not missing at random
视频:
A Linear Reinforcement Learning Algorithm 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