On the Impact of Item Representation in the Quality of Recommendations
Andre Carvalho, Hendrik Macedo
Journal of Intelligent Computing, Vol. 8, No. 3, 2017.
DOI: 
Link: http://www.dline.info/jic/fulltext/v8n3/jicv8n3_1.pdf
Abstract:
Most of data about an item in recommendation algorithms actually consists of meta-information. We argue that
meta-information heavily affects the recommendation quality. This paper analyses the impact of deeper representations and
more elaborate similarity measures in the quality of the recommendation of music. Firstly, we propose a innovative approach
to extract musical features from audio files and use it to deepen song representation. Next, we propose a newer metric for
measuring diversification. Finally, we evaluate both diversification and accuracy for the recommendation of songs. Results
show that the inclusion of these deeper features on the representation of the item improves the accuracy and greatly improves
diversification.