Building Multimodal Deep learning recommendation Systems by Sujoy Roychowdhury #ODSC_India

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 itemsin order to get to what they're looking for. The data on how usersinteract 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, untilrecently, been able to achieve personalization through user basedand item based collaborative filtering techniques. Recent advancesin 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 arealmost invariably incomplete in real-world datasets due to variousprocess 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 andthus is our choice in implementing multi-modal recommendation system. In this talk we show a real-world application of a fashion recommendationsystem. 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 andhow these trade-offs affect the actual choice of the architecture.

More details: https://confengine.com/odsc-india-2019/proposal/9852

Conference Link: https://india.odsc.com

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