My paper "RecStore: An Extensible and Adaptive Framework for Online Recommender Queries inside the Database Engine" has been recently accepted to the International Conference on Extending Database Technology. The paper is a joint work with Justin J. Levandoski, Mohamed F. Mokbel, and Micheal D. Ekstrand. Here is the paper abstract:
Most non-trivial recommendation methods (e.g., collaborative filtering) consist of (1) a computationally intense offline phase that computes a recommender model based on users' opinions of items, and (2) an online phase consisting of SQL-based queries that use the model (generated offline) to derive user preferences and provide recommendations for interesting items. Current application usage trends require a completely online recommender process, meaning the recommender model must update in real time as new opinions enter the system. To tackle this problem, we propose RecStore, a DBMS storage engine module capable of efficient online model maintenance. Externally, models managed by RecStore behave as relational tables, thus existing SQL-based recommender queries remain unchanged while gaining online model support. RecStore maintains internal statistics and data structures aimed at providing efficient incremental updates to the recommender model, while employing an adaptive strategy for internal maintenance and load shedding to realize a tuneable tradeoff for more efficient updates or query processing based on system workloads. RecStore is also extensible, supporting a declarative syntax for defining recommender models. The efficacy of RecStore is demonstrated by providing the implementation details of five state-of-the-art collaborative filtering models. Performance benefits are demonstrated through extensive experimental evaluation of a prototype of RecStore, built inside the storage engine of PostgreSQL, using a real-life recommender system workload.
I will be presenting the paper at EDBT 2012 in Berlin Germany on March 27th, 2012. will be glad to receive your comments