Mehdi Hosseinzadeh Aghdam

Recommendation systems (RSs) usually create suggestions based on users’ prior intentions. Users’ interests may evolve due to context change or user-mode change. Discovering such a change is crucial for producing personalized suggestions. Traditional approaches assume that each user has a fixed preference. On the contrary, context-aware recommendation systems (CARSs) use contextual information to detect user intention changes. However, applying contextual information is the main challenge in CARSs, because it is not always feasible to achieve all the users’ contextual information. Furthermore, adding different contexts to RSs grows its dimensionality in multiple applications. Besides, existing CARSs cannot precisely obtain the hierarchical relationships between items and contexts items that influence users’ intentions. They often use short-term interest with either static long-term preference in the recommendation process. To alleviate the mentioned challenges, we propose a novel deep context-aware recommendation system (DCARS) to capture and incorporate user preferences changes in the recommendation process. The proposed method models the latent context among selected items in each session throughout users’ historical interactions and combines users’ short-term and long-term preferences to generate recommendations. Specifically, we suggest a DCARS based on latent representations of sessions derived from users’ activities. The experiment results on benchmark context-aware data sets show that the proposed DCARS model surpasses state-of-the-art approaches.