Add the STAN algorithm to Serenade.
Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN https://dl.acm.org/doi/10.1145/3331184.3331322
This method called “Sequence and Time Aware Neighborhood” was presented at SIGIR ’19.
STAN is based on SKNN, but it additionally takes into account the following factors for making recommendations: i) the position of an item in the current session, ii) the recency of a past session w.r.t. to the current session, and iii) the position of a recommendable item in a neighboring session. Their results show that STAN significantly improves over SKNN.
Its very likely that the STAN algorithm can leverage the VMISIndex to do its computation.
A python implementation of STAN can be found here:
https://github.com/rn5l/session-rec/blob/master/algorithms/knn/stan.py
Add the STAN algorithm to Serenade.
Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN https://dl.acm.org/doi/10.1145/3331184.3331322
This method called “Sequence and Time Aware Neighborhood” was presented at SIGIR ’19.
STAN is based on SKNN, but it additionally takes into account the following factors for making recommendations: i) the position of an item in the current session, ii) the recency of a past session w.r.t. to the current session, and iii) the position of a recommendable item in a neighboring session. Their results show that STAN significantly improves over SKNN.
Its very likely that the STAN algorithm can leverage the VMISIndex to do its computation.
A python implementation of STAN can be found here:
https://github.com/rn5l/session-rec/blob/master/algorithms/knn/stan.py