## 2 Background and Related Work
### Time-Aware Recommender Systems
#### 2.2.2 Temporal Context
Tourism is one of these domains, given that data regarding factors like location, time and weather, obtained through these devices, are essential for recommending activities or tourist points of interest [62; 63]. In most of the work involving this category, time is only studied from the point of view of the context, without considering time dynamics. Some proposals [64; 65] consider three temporal aspects, the periodicity, consecutiveness, and non-uniformness, for making point of interest (POI) recommendations in location-based social networks (LBSNs). In [64], an aggregated temporal tensor factorization (ATTF) model is proposed to capture those temporal features. The recommendations of POIs at a specific time are based on user patterns involving the temporal features. The POI check-ins made by users on the social network are used as implicit feedback. The same temporal aspects are studied in [65], where the concept of a temporal subset property (TSP) is introduced to designate the time dimensions comprised for granular slots, where some of them being subsets of others. In this work, the multi-aspect time-related influence (MATI) probabilistic model is proposed for location recommendations. It is a multivariate model that simultaneously considers multiple latent temporal parameters. These methods are highly focused on aspects related to points of interest (restaurants, bars, etc. ), as well as on the behavioral patterns of users when visiting these places. Therefore, it would be difficult to extend the use of these models to other application domains. Furthermore, although some aspects of temporal dynamics are considered, the evolution of user preferences is not one of them. In the field of music, contextual aspects such as the time of day, season, weather or mood, have a particular importance with respect to the type of music listened to by the user. Therefore, these contextual conditions should be taken into account in order to improve user recommendations. In this regard, more and more methods are emerging that allow recommender systems to adapt to specific contexts [61]. One of these is the Lifetrak system [66], which generates a playlist from the user's music library based on factors related to the user's physical context such as the location, time, day, traffic, level of noise, temperature and weather. Song recommendations in specific time contexts is the objective of a time-aware CF proposal, where micro-profiles are created by using contextual pre-filtering [67]. In this work, different time splits are tested to obtain micro-profiles that best represent the behavior of users during each period. A dataset from Last.fm was used to validate the proposed method. In [68], emotional states are detected by using information inferred from the context of a user at a given time. The contextual recommendation of songs is based on those emotional states. A mood based recommendation of music for videos is proposed in [69]. Both videos and music are projected onto a latent emotional space and a latent factor model is used to find the relevance between videos and music. The time factor is also the target of some context-aware music recommender systems. In [70], music preferences are dynamically adapted to the contextual changes associated with the users. A sequential pattern algorithm is used to capture contextual states. In [71], changes in user preferences are modeled using hidden Markov models. The sequence of items obtained from the user is used to obtain the model, whose hidden variables represent the context of the user. The context prediction is taken to generate the recommendations. With this proposal, which is validated with data from music playlists, it is possible to improve the accuracy and diversity of the results provided using other methods. [PERSON] et al. [72] proposed a sequence-based method for context-aware music recommendations that combines the general and contextual preferences obtained from users' previous listening sessions.