Publications
Authors:
  • Jullien Gaillard , Jean-Michel Renders
Citation:
ECIR, Vienna, Austria, March 29 - April 02, 2015.
Abstract:
Real-world Recommender Systems are often facing drifts in
users' preferences and shifts in items' perception or use. Traditional stateof-
the-art methods based on matrix factorization are not originally designed
to cope with these dynamic and time-varying e ects and, indeed,
could perform rather poorly if there is no "reactive", on-line model update.
In this paper, we propose a new incremental matrix completion
method, that automatically allows the factors related to both users and
items to adapt on-line" to such drifts. Model updates are based on a
temporal regularization, ensuring smoothness and consistency over time,
while leading to very ecient, easily scalable algebraic computations.
Several experiments on real-world data sets show that these adaptation
mechanisms signi cantly improve the quality of recommendations
compared to the static setting and other standard on-line adaptive algorithms
Year:
2015
Report number:
2015/004
Attachments: