Unified Embedding Model over Heterogeneous Information Network for
Personalized Recommendation
Abstract
Most of heterogeneous information network (HIN)
based recommendation models are based on the
user and item modeling with meta-paths. However, they always model users and items in isolation under each meta-path, which may lead to information extraction misled. In addition, they only
consider structural features of HINs when modeling users and items during exploring HINs, which
may lead to useful information for recommendation lost irreversibly. To address these problems,
we propose a HIN based unified embedding model
for recommendation, called HueRec. We assume
there exist some common characteristics under different meta-paths for each user or item, and use
data from all meta-paths to learn unified users’ and
items’ representations. So the interrelation between
meta-paths are utilized to alleviate the problems of
data sparsity and noises on one meta-path. Different from existing models which first explore HINs
then make recommendations, we combine these
two parts into an end-to-end model to avoid useful
information lost in initial phases. In addition, we
embed all users, items and meta-paths into related
latent spaces. Therefore, we can measure users’
preferences on meta-paths to improve the performances of personalized recommendation. Extensive experiments show HueRec consistently outperforms state-of-the-art methods