Feature Evolution Based Multi-Task Learning for Collaborative
Filtering with Social Trust
Abstract
Social recommendation could address the data
sparsity and cold-start problems for collaborative
filtering by leveraging user trust relationships as
auxiliary information for recommendation. However, most existing methods tend to consider the
trust relationship as preference similarity in a static
way and model the representations for user preference and social trust via a common feature space.
In this paper, we propose TrustEV and take the
view of multi-task learning to unite collaborative
filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for
two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller
to globally explore proper settings for the feature
evolution units. The training process contains two
nested loops, where in the outer loop, we optimize
the meta-controller by Bayesian optimization, and
in the inner loop, we train the feedforward model
with given feature evolution units. Experiment results show that TrustEV could make better use of
social information and greatly improve recommendation MAE over state-of-the-art approaches