摘要: I thought the low train AUC was due to the underfitting, but after some experiments I found that it is not as thought. The low train AUC was caused by 阅读全文
posted @ 2017-08-29 20:14 Fassy 阅读(218) 评论(0) 推荐(0) 编辑
摘要: Finally succeeded in optimizing the codes of lightfm model! But the computational cost is very high, so I wil use only 1000/227427 of all the checkins 阅读全文
posted @ 2017-08-28 21:44 Fassy 阅读(301) 评论(0) 推荐(0) 编辑
摘要: Just Keep a record of the running tasks, I have already begun to get confused..... All these are based on the files of cluster : On going: Job 32415:f 阅读全文
posted @ 2017-08-24 15:07 Fassy 阅读(257) 评论(0) 推荐(0) 编辑
摘要: data used :foursquare NYC data, this code has not been finished yet.. here is the link: https://github.com/FassyGit/LightFM_liu/blob/master/DomainBias 阅读全文
posted @ 2017-08-11 23:05 Fassy 阅读(125) 评论(0) 推荐(0) 编辑
摘要: Here is the code link: https://github.com/FassyGit/LightFM_liu/blob/master/U_F1.py I use NYC data as other experimens. The split of the training data 阅读全文
posted @ 2017-08-11 22:51 Fassy 阅读(185) 评论(0) 推荐(0) 编辑
摘要: Tutorials for Recommender Systems: 1.Implementing your own recommender systems in python 2.Beginners' guide to Non-negative Matrix Factorization 3.Alt 阅读全文
posted @ 2017-08-08 17:57 Fassy 阅读(173) 评论(0) 推荐(0) 编辑
摘要: 1.test on four square data a) Binary matrix b) Matrix counting in the times c) Hybrid Model d) consider the context (for example:time, location) 2. Si 阅读全文
posted @ 2017-08-08 17:31 Fassy 阅读(274) 评论(0) 推荐(0) 编辑
摘要: Here is a link that explains the cosine similarity and cosine pairwise distances. https://stackoverflow.com/questions/35281691/scikit-cosine-similarit 阅读全文
posted @ 2017-08-08 15:35 Fassy 阅读(358) 评论(0) 推荐(0) 编辑
摘要: Experiments on the NYC datasets, here is the dataset link: https://sites.google.com/site/yangdingqi/home/foursquare-dataset Forgive me being lazy and 阅读全文
posted @ 2017-08-03 20:11 Fassy 阅读(281) 评论(0) 推荐(0) 编辑
摘要: I read this paper, the purpose are common to some extent...but the way this paper has adapted and the way we discussed yesterday still have many diffe 阅读全文
posted @ 2017-08-01 18:35 Fassy 阅读(232) 评论(0) 推荐(0) 编辑