Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that the features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems (RecSys 18

Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuarios

Comprender la intención de búsqueda del usuario permite identificar y extraer los resultados de búsqueda más relevantes y personalizados de la información disponible según sus necesidades. En el presente artículo se plantea un algoritmo para la recuperación de información relevante que combina las preferencias del perfil del usuario y la expansión de consulta para obtener resultados de búsqueda relevantes y personalizados. El proceso de recuperación de información se valida mediante las métricas de Precision, Recall y Mean Average Precision (MAP) aplicadas a un conjunto de datos que contiene los documentos estandarizados y los perfiles de preferencias. Los resultados permitieron demostrar que el algoritmo mejora el proceso de recuperación de información al arrojar documentos con mejor calidad y relevancia según las necesidades de los usuarios