Fast Offline Policy Optimization for Large Scale Recommendation. (arXiv:2208.05327v2 [cs.IR] UPDATED)

Personalised interactive systems such as recommender systems require
selecting relevant items dependent on context. Production systems need to
identify the items rapidly from very large catalogues which can be efficiently
solved using maximum inner product search technology. Offline optimisation of
maximum inner product search can be achieved by a relaxation of the discrete
problem resulting in policy learning or reinforce style learning algorithms.
Unfortunately this relaxation step requires computing a sum over the entire
catalogue making the complexity of the …

arxiv optimization policy recommendation scale