摘要：some results running on the linux laptop with the new model: radius: 10 max_sampled: 10 the same data with the original lightfm model: Radius = 28 lin
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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
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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
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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
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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
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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
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摘要：Tutorials for Recommender Systems: 1.Implementing your own recommender systems in python 2.Beginners' guide to Non-negative Matrix Factorization 3.Alt
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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
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摘要：Here is a link that explains the cosine similarity and cosine pairwise distances. https://stackoverflow.com/questions/35281691/scikit-cosine-similarit
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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
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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
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摘要：Some problems : Expect to get a binary marix but no... Here is the code in console: and Running on cluster for one night but got no results showed...
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