  With the prevalence of live broadcast business nowadays, a new type of
recommendation service, called live broadcast recommendation, is widely used in
many mobile e-commerce Apps. Different from classical item recommendation, live
broadcast recommendation is to automatically recommend user anchors instead of
items considering the interactions among triple-objects (i.e., users, anchors,
items) rather than binary interactions between users and items. Existing
methods based on binary objects, ranging from early matrix factorization to
recently emerged deep learning, obtain objects' embeddings by mapping from
pre-existing features. Directly applying these techniques would lead to limited
performance, as they are failing to encode collaborative signals among
triple-objects. In this paper, we propose a novel TWo-side Interactive NetworkS
(TWINS) for live broadcast recommendation. In order to fully use both static
and dynamic information on user and anchor sides, we combine a product-based
neural network with a recurrent neural network to learn the embedding of each
object. In addition, instead of directly measuring the similarity, TWINS
effectively injects the collaborative effects into the embedding process in an
explicit manner by modeling interactive patterns between the user's browsing
history and the anchor's broadcast history in both item and anchor aspects.
Furthermore, we design a novel co-retrieval technique to select key items among
massive historic records efficiently. Offline experiments on real large-scale
data show the superior performance of the proposed TWINS, compared to
representative methods; and further results of online experiments on Diantao
App show that TWINS gains average performance improvement of around 8% on ACTR
metric, 3% on UCTR metric, 3.5% on UCVR metric.
