Deep Learning Based Personalized Stock Recommender System

This research paper introduces a personalized recommender system tailored specifically for the stock market. With the increasing complexity and variety of investment options, individual investors face significant challenges in making informed decisions. Traditional stock market recommendations often offer generic advice that fails to account for investors’ unique preferences and risk appetites. We propose a personalized recommender system that utilizes deep learning techniques to provide customized stock recommendations. Our approach combines collaborative filtering (CF) and content-based (CB) filtering methodologies which form a hybrid system capable of generating personalized recommendations. Collaborative filtering utilizes the behaviour of similar investors to identify stocks that are likely to appeal to the user, while content-based filtering matches stock characteristics with the user’s preferences and investment history. Experimental evaluations demonstrate that the proposed personalized recommender system outperforms existing algorithms and approaches trained on user interaction data taken from the stock market domain, providing investors with tailored stock recommendations that align with their personal needs and preferences.