Recommendation systems aid in consumer decision making processes
like what to buy, which books to read or movies to watch.
Recommendation systems are specially useful in e-commerce websites
where a user has to navigate through several hundred items
in order to get to what they’re looking for . The data on how users
interact with the systems can be used to analyze user behaviour and
make recommendations that are in line with users’ preferences of
certain item attributes over others. Collaborative filtering has, until
recently, been able to achieve personalization through user based
and item based collaborative filtering techniques. Recent advances
in the application of Deep Learning in research as well as industry
has led people to apply these techniques in recommendation systems.
Many recommendation systems use product features for recommendations.
However textual features available on products are
almost invariably incomplete in real-world datasets due to various
process related issues. Additionally, product features even when
available cannot describe completely a certain feature. These limit
the success of such recommendation techniques. Deep learning
systems can process multi-modal data like text, images, audio and
thus is our choice in implementing multi-modal recommendation
system.
In this talk we show a real-world application of a fashion recommendation
system. This is based on a multi-modal deep learning system which is able to address the problem of poor annotation in the product data. We evaluate different deep learning architectures
to process multi-modal data and compare their effectiveness. We
highlight the trade-offs seen in a real-world implementation and
how these trade-offs affect the actual choice of the architecture.