awarebayes/RecNN

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

miladfa7 opened this issue · 9 comments

Is this a complete implementation of the paper (Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling)?

Not yet, but the ddpg actor itself is working. Tomorrow I will add regularization and state representation (I deleted it recently for simplicity purposes). The dataset will be available soon. The only thing left from ML20 are the rewards (ratings) and if there are any problems with copyright I will release a tree based solution to generate synthetic ratings. I also don't know about the user embeddings just yet. But I wanted to make it more than just 'code for paper' by adding graph and geometrical learning support as well as overhauling the dataset

very good
thanks

Most of the recommender systems use lstm for state representation. What if you use finite item state representation with novel importance sampling algorithms, (i.e.) state is sampled from learning to rank (LambdaRank, RankNet) or some Bayesian based algorithm.
Lstm is not the best idea for state representation/1D convolution. Wavenets switched to TCNs a while ago: “The fall of RNN / LSTM” by Eugenio Culurciello https://link.medium.com/EoheS55VFZ
P.S. if you happen to implement any of these, please push a commit

Hi there, sweetheart!
I got some cool mentoring in MIPT here in Russia on general Deep Learning in the junior year of high school, but I studied the RL by myself. TBH when I wrote my first article I was not very familiar with Reinforcement Learning, just wanted to do a cool school project and tell about something.


Check out Yandexes Practical RL: github.com/yandexdataschool/Practical_RL
They have materials linked in the lesson folder as well as interactive notebooks


Read Andrej Karpathy's blog: http://karpathy.github.io/


Jonathan Hui has some cool blogposts on general RL: https://medium.com/@jonathan_hui/rl-deep-reinforcement-learning-series-833319a95530


You can visit our website: ttps://www.dlschool.org/?lang=en
Or/And fork the repo: https://github.com/DLSchool/dlschool_english