Modeling User Exposure in Recommendation
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
arXiv.org
Modeling User Exposure in Recommendation
Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...
RecSim: A Configurable Simulation Platform for Recommender Systems
It's a configurable platform for authoring simulation environments to facilitate the study of RL algorithms in recommender systems (and CIRs in particular).
RᴇᴄSɪᴍ allows both researchers and practitioners to test the limits of existing RL methods in synthetic recommender settings. RecSim’s aim is to support simulations that mirror specific aspects of user behavior found in real recommender systems and serve as a controlled environment for developing, evaluating and comparing recommender models and algorithms, especially RL systems designed for sequential user-system interaction.
As an open-source platform, RᴇᴄSɪᴍ:
* facilitates research at the intersection of RL and recommender systems
* encourages reproducibility and model-sharing
* aids the recommender-systems practitioner, interested in applying RL to rapidly test and refine models and algorithms in simulation, before incurring the potential cost (e.g., time, user impact) of live experiments
* serves as a resource for academic-industry collaboration through the release of “realistic” stylized models of user behavior without revealing user data or sensitive industry strategies.
blog: https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html
paper: https://arxiv.org/abs/1909.04847
code: https://github.com/google-research/recsim
#rs #rl
It's a configurable platform for authoring simulation environments to facilitate the study of RL algorithms in recommender systems (and CIRs in particular).
RᴇᴄSɪᴍ allows both researchers and practitioners to test the limits of existing RL methods in synthetic recommender settings. RecSim’s aim is to support simulations that mirror specific aspects of user behavior found in real recommender systems and serve as a controlled environment for developing, evaluating and comparing recommender models and algorithms, especially RL systems designed for sequential user-system interaction.
As an open-source platform, RᴇᴄSɪᴍ:
* facilitates research at the intersection of RL and recommender systems
* encourages reproducibility and model-sharing
* aids the recommender-systems practitioner, interested in applying RL to rapidly test and refine models and algorithms in simulation, before incurring the potential cost (e.g., time, user impact) of live experiments
* serves as a resource for academic-industry collaboration through the release of “realistic” stylized models of user behavior without revealing user data or sensitive industry strategies.
blog: https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html
paper: https://arxiv.org/abs/1909.04847
code: https://github.com/google-research/recsim
#rs #rl
