  Factorization Machines (FMs) are effective in incorporating side information
to overcome the cold-start and data sparsity problems in recommender systems.
Traditional FMs adopt the inner product to model the second-order interactions
between different attributes, which are represented via feature vectors. The
problem is that the inner product violates the triangle inequality property of
feature vectors. As a result, it cannot well capture fine-grained attribute
interactions, resulting in sub-optimal performance. Recently, the Euclidean
distance is exploited in FMs to replace the inner product and has delivered
better performance. However, previous FM methods including the ones equipped
with the Euclidean distance all focus on the attribute-level interaction
modeling, ignoring the critical intrinsic feature correlations inside
attributes. Thereby, they fail to model the complex and rich interactions
exhibited in the real-world data. To tackle this problem, in this paper, we
propose a FM framework equipped with generalized metric learning techniques to
better capture these feature correlations. In particular, based on this
framework, we present a Mahalanobis distance and a deep neural network (DNN)
methods, which can effectively model the linear and non-linear correlations
between features, respectively. Besides, we design an efficient approach for
simplifying the model functions. Experiments on several benchmark datasets
demonstrate that our proposed framework outperforms several state-of-the-art
baselines by a large margin. Moreover, we collect a new large-scale dataset on
second-hand trading to justify the effectiveness of our method over cold-start
and data sparsity problems in recommender systems.
