Context-Aware Recommender Systems

Abstract

Declaración de obra originalYo declaro lo siguiente:He leído el Acuerdo 035 de 2003 del Consejo Académico de la Universidad Nacional. «Reglamento sobre propiedad intelectual» y la Normatividad Nacional relacionada al respeto de los derechos de autor. Esta disertación representa mi trabajo original, excepto donde he reconocido las ideas, las palabras, o materiales de otros autores.Cuando se han presentado ideas o palabras de otros autores en esta disertación, he realizado su respectivo reconocimiento aplicando correctamente los esquemas de citas y referencias bibliográficas en el estilo requerido.He obtenido el permiso del autor o editor para incluir cualquier material con derechos de autor (por ejemplo, tablas, figuras, instrumentos de encuesta o grandes porciones de texto).Por último, he sometido esta disertación a la herramienta de integridad académica, definida por la universidad. ________________________________ JUAN FELIPE MUÑOZ FERNÁNDEZFecha 23/02/2021 Agradecimientos Al Ingeniero Juan Carlos Restrepo Carvajal por sus aportes en las discusiones alrededor de todos los temas que enmarca esta tesis.

Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

“…The importance of considering user characteristics in recommender systems has been highlighted many times [1]. In the music domain, relying only on listening histories or user ratings is nevertheless still the most widely adopted approach to build collaborative filtering algorithms, even though recent work shows that integrating additional listener or listening information is beneficial [2], [3].…”

“…Please note that the LFM-1b User Genre Profile is considered derivative work according to paragraph 4.1 of Last.fm's API Terms of Service and can therefore be "published, distributed or otherwise communicated to the public in any media known now". 1 In the following, we detail data acquisition, creation, and content of the dataset (Section II), provide insights gained through statistical analyses of the genre profiles (Section III), and conclude with a discussion of the dataset's limitations and possible extensions (Section IV).…”

SOS: A multimedia recommender System for Online Social networks

“…Eventually, a recommendation strategy should be able to provide users with relevant information depending on the context [20,9,21] (i.e. user location, observed items, etc.)…”

Plant@Hand

“…Recommender systems are a subclass of the information filtering systems that attempt to predict a "rating" or "preference" that a user would assign to an item. Traditional recommender systems disregard the notion of "situated actions" [18], the fact that users interact with the system within a particular "context" and that preferences for items within one context may be different from those in another context [3]. They produce a list of recommendations by collaborative or content-based filtering.…”