Sequential Recommendation Using Convolutional Neural Networks

Abstract

With the prevalence of information technology (IT), recommender system has long been acknowledged as an effective tool for addressing information overload problem, which makes users easily filter and locate information of their preferences, and allows online platforms to widely publicize the information they produce. In the field of sequential recommendation, deep learning methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. In this view, this thesis focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically, we illustrate the concept of sequential recommendation, propose a convolutional neural network model to show the effectiveness of sequential recommenders based on CNNs in several real-life scenarios.