A Practical Solution to the ACM RecSys Challenge 2018

Abstract

Cold-start problem, occurring when a new user joins the system, is an important factor that influences the satisfaction of the users. Since the user has no or almost no interaction with the recommendation system before, the system finds it difficult to obtain adequate information about user preferences so that it cannot provide a high quality personalized recommendation service. Furthermore, there is also a lack of information about the preferences of even familiar users due to the fact that many of them are not always willing to explicitly describe their evaluation of a specific item through ratings. Utilizing all information about user preferences including explicit and especially implicit data helps our recommendation system achieve a promising result in the ACM RecSys Challenge 2018 organized by Spotify. Experiments show that the proposed model not only deals with the cold-start problem but also gains a high precision of recommendation for the whole system whilst costing an amount of much lower time and hardware resource compared with Top 5 participants.