264 research outputs found
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in sequence. In this work we present DJ-MC, a novel
reinforcement-learning framework for music recommendation that does not
recommend songs individually but rather song sequences, or playlists, based on
a model of preferences for both songs and song transitions. The model is
learned online and is uniquely adapted for each listener. To reduce exploration
time, DJ-MC exploits user feedback to initialize a model, which it subsequently
updates by reinforcement. We evaluate our framework with human participants
using both real song and playlist data. Our results indicate that DJ-MC's
ability to recommend sequences of songs provides a significant improvement over
more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at
AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems
(AAMAS) 2015, Istanbul, Turkey, May 201
A personality-aware group recommendation system based on pairwise preferences
Human personality plays a crucial role in decision-making and it has paramount importance
when individuals negotiate with each other to reach a common group decision.
Such situations are conceivable, for instance, when a group of individuals want to watch
a movie together. It is well known that people influence each other’s decisions, the more
assertive a person is, the more influence they will have on the final decision. In order to
obtain a more realistic group recommendation system (GRS), we need to accommodate
the assertiveness of the different group members’ personalities. Although pairwise preferences
are long-established in group decision-making (GDM), they have received very little
attention in the recommendation systems community. Driven by the advantages of pairwise
preferences on ratings in the recommendation systems domain, we have further pursued
this approach in this paper, however we have done so for GRS. We have devised a
three-stage approach to GRS in which we 1) resort to three binary matrix factorization
methods, 2) develop an influence graph that includes assertiveness and cooperativeness
as personality traits, and 3) apply an opinion dynamics model in order to reach consensus.
We have shown that the final opinion is related to the stationary distribution of a Markov
chain associated with the influence graph. Our experimental results demonstrate that our
approach results in high precision and fairness.Spanish Government PID2019-10380RBI00/AEI/10. 13039/501100011033Andalusian Government P20_0067
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