  Click-through rate (CTR) prediction is a critical task in online display
advertising. The data involved in CTR prediction are typically multi-field
categorical data, i.e., every feature is categorical and belongs to one and
only one field. One of the interesting characteristics of such data is that
features from one field often interact differently with features from different
other fields. Recently, Field-aware Factorization Machines (FFMs) have been
among the best performing models for CTR prediction by explicitly modeling such
difference. However, the number of parameters in FFMs is in the order of
feature number times field number, which is unacceptable in the real-world
production systems. In this paper, we propose Field-weighted Factorization
Machines (FwFMs) to model the different feature interactions between different
fields in a much more memory-efficient way. Our experimental evaluations show
that FwFMs can achieve competitive prediction performance with only as few as
4% parameters of FFMs. When using the same number of parameters, FwFMs can
bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.
