A Joint Embedding Technique for Sequential Recommendation
Lakshmi Narayana Pothuraju, Kiran Kumar Pattanaik, Rajendra Sahu · 2020
From many e-commerce websites such as Amazon, Flipkart and Netflix to online advertisements, recommender systems are used to recommend certain products to their users depending on their preferences and interactions with the products. Sequential recommender system is useful to model the short term behavior of the users depending on their latest interactions. Many techniques ranging from Markov chain models to convolutional neural networks (CNNs) have been used to solve the sequential recommendation problem. The most efficient state-of-the-art-model that uses CNNs is the top-N sequential recommendation system [1]. This model fails to capture the skip behavior in the union-level patterns as it doesn't take the interaction between distant items in a sequence into consideration. This paper adopts the basic architecture proposed in Caser [1] and introduction of the joint embedding of two items, formation of a 3D tensor with all the embeddings and a introduction of a new convolutional block have been made. This novel approach improves the mean average precision (MAP) from 0.1507 to 0.1884 which is significant and gives rise to the most efficient sequential recommendation system that uses CNN.