A major challenge in recommender systems is handling new users, whom are also called @math users. In this paper, we propose a novel approach for learning an optimal series of questions with which to interview cold-start users for movie recommender systems. We propose learning interview questions using Deep Q Networks to create user profiles to make better recommendations to cold-start users. While our proposed system is trained using a movie recommender system, our Deep Q Network model should generalize across various types of recommender systems.
In @cite_3 , the authors build on the previous work and propose . In this, latent profiles are associated for each node of the decision tree and hence the user profile is the function of all possible answers to the interview. The novelty is an iterative optimization algorithm that alternates between decision tree construction and latent profiles' generation. This helps to learn the best decision tree to ask questions and generate good embeddings simultaneously. The profile @math is tied to user @math ’s responses in the form of a function; thus the name functional matrix factorization (fMF). Given an answer set @math , the user profile is generated by @math . The goal is to learn T and @math (item profiles) from the observed ratings set.
@cite_3
2018571751
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree --- in effect restricting the latent profiles to be a function of possible answers to the interview questions --- which allows the profiles to be gradually refined through the interview process based on user responses. We develop an iterative optimization algorithm that alternates between decision tree construction and latent profiles extraction as well as a regularization scheme that takes into account of the tree structure. Experimental results on three benchmark recommendation data sets demonstrate that the proposed fMF algorithm significantly outperforms existing methods for cold-start recommendation.