Hitherto, temporal aspects of user activity in Recommender Systems were used in two different scenarios: explicit feedback and implicit feedback. The first one is related to explicitly expressing ratings for movies, for example: Netflix prize data set contains time stamps associated with the ratings. As it was shown using them improved rating prediction. On the other hand, there is an implicit feedback data (e.g. e-commerce logs that describe user shopping behavior), which contain time stamps that also can be used in identifying user patterns (when the user tends to purchase more in the morning and towards the evening; on Mondays rather than the middle of the week, before the holidays on August rather than other months and so on), building user profiles, identifying similar users (for CF) and use all this useful information for items to purchase recommendations. Not only e-commerce, but other domains with web click streams, can be analysed considering temporal components. In recent years’ Markovian model and sequential pattern-mining methods were frequently used for such tasks. Recently temporal graphs and Recurrent Neural Networks are also considered for sequential data analyses and providing recommendations for people, communities, locations, etc.

The workshop aims at bringing together researchers and practitioners working on temporal aspects in Recommender Systems domain in order to look at the challenges from the point of view of the temporal aspects in Recommender Systems and user modelling in order to provide relevant (often personalized) recommendations regarding the representation and reasoning about temporal aspects. All in all, the workshop aims at attracting presentations of novel ideas for addressing these challenges and how to advance the current state of the art in this field.