  Music listening preferences at a given time depend on a wide range of
contextual factors, such as user emotional state, location and activity at
listening time, the day of the week, the time of the day, etc. It is therefore
of great importance to take them into account when recommending music. However,
it is very difficult to develop context-aware recommender systems that consider
these factors, both because of the difficulty of detecting some of them, such
as emotional state, and because of the drawbacks derived from the inclusion of
many factors, such as sparsity problems in contextual pre-filtering. This work
involves the proposal of a method for the detection of the user contextual
state when listening to music based on the social tags of music items. The
intrinsic characteristics of social tagging that allow for the description of
items in multiple dimensions can be exploited to capture many contextual
dimensions in the user listening sessions. The embeddings of the tags of the
first items played in each session are used to represent the context of that
session. Recommendations are then generated based on both user preferences and
the similarity of the items computed from tag embeddings. Social tags have been
used extensively in many recommender systems, however, to our knowledge, they
have been hardly used to dynamically infer contextual states.

Information Retrieval