  The consumption of music has its specificities in comparison with other
media, especially in relation to listening durations and replays. Music
recommendation can take these properties into account in order to predict the
behaviours of the users. Their impact is investigated in this paper. A large
database was thus created using logs collected on a streaming platform, notably
collecting the listening times. The proposed study shows that a high proportion
of the listening events implies a skip action, which may indicate that the user
did not appreciate the track listened. Implicit like and dislike can be deduced
from this information of durations and replays and can be taken into account
for music recommendation and for the evaluation of music recommendation
engines. A quantitative study as usually found in the literature confirms that
neighborhood-based systems considering binary data give the best results in
terms of MAP@k. However, a more qualitative evaluation of the recommended
tracks shows that many tracks recommended, usually evaluated in a positive way,
lead to skips or thus are actually not appreciated. We propose the
consideration of implicit like/dislike as recommendation engine inputs.
Evaluations show that neighbourhood-based engines remain the most precise, but
filtering inputs according to durations and/or replays have a significant
positive impact on the objective of the recommendation engine. The
recommendation process can thus be improved by taking account of listening
durations and replays. We also study the possibility of post-filtering a list
of recommended tracks so as to limit the number of tracks that will be
unpleasantly listened (skip and implicit dislike) and to increase the
proportion of tracks appreciated (implicit like). Several simple algorithms
show that this post-filtering operation leads to an improvement of the quality
of the music recommendations.
