Combining Context Features in Sequence-Aware Recommender Systems
Published in
1 min readAug 28, 2019
presented in RecSys 2019 Late-breaking Results, 16th-20th September 2019, Copenhagen, Denmark by Sarai Mizrachi and Pavel Levin
Paper: http://ceur-ws.org/Vol-2431/paper3.pdf
There are several important design choices that machine learning practitioners need to make when incorporating predictors into RNN-based contextual recommender systems. A great deal of currently reported findings about these decisions focus on the setting where predicted items take on values from the space of sequence items. This work provides an empirical evaluation of some straightforward approaches of dealing with such problems on a real-world large scale prediction problem from the travel domain, where predicted entities do not live in the space of sequence items.
