## 4 Results
### Temporal Models and Recommendation Approaches
#### 4.3.1 Time-Dependent Model
For example, [66] proposed a tensor-based method by using timestamped data with different periodic patterns. Additionally, they explored the bipartite graphs to model the evolution of user preferences over time. It was shown that the tensor-based methods are effective for learning the dynamics of user preferences. In another contribution to context-aware RSs, [132] created an optimally ranked list of items for each user considering the dynamic of user preference by directly training the tensor model for such purposes. In [133], the authors proposed a CP tensor factorization that uses a smoothing factor to reduce the weight of the user-item interaction data in a time interval according to the observed levels of changes in user preferences. This implies, that the tensor is trained to capture the user's changing habits based on a weighting scheme wherethe user's past preferences are given less weight and the current preferences receive more weight. Although, the tensor factorization offers a well-structured and principled model to incorporate the temporal dynamics in RSs, however, it is limited by the flexibility of the model structure, which makes it difficult to process and decompose especially for the large-scale and sparse tensors. In order to overcome those limitations, this approach was later extended by exploiting the user's side data such as demographic information on a coupled tensor-matrix factorization method to improve the accuracy of recommendations [74]. _Short/Long-term preference modeling:_ SLPM is another way of approaching sequentially ordered data which focuses on how to separately model the user' short-/and long-term behavior respectively [36; 66; 133; 134]. The SLPM is based on the assumption that each user has potentially two models, one for short-term preferences and another for long-term preferences [26]. To this end, [36] stressed the importance of short-term preference model to re-rank the recommendations lists which were proven to have a considerable improvement in terms of accuracy. In a different approach, [66] proposed a model consisting of an offline component and online component to capture the long-term and short-term influences. With the online component, the short-term influence is continuously updated with new incoming data and therefore tends to be more sensitive to the less stable or short-term preferences. While in the offline component, the long-term influence which contains more stable preferences is updated much less frequently using the data meanwhile stored in the online component. In [135], a Long-/and Short-term algorithm (LOGO) was used to partition a users' reading historic data into multiple time slices to build both the long-/and short-term preference model using a time-sensitive weighting scheme. [45] provided the extension of this method to construct the user profile in a more efficient way, using different time functions and profiling methods. The experiment shows that the proposed method can provide a high quality of DRS results. _Data-stream algorithms:_ These methods focused on maintaining an up-to-date model in a streaming environment, mostly characterized by continuous and high-speed data processing tasks. For proper learning of stream data with concept drift in RS field, this approach was fairly explored by few studies [3; 65; 76; 136; 137]. For example, [3] proposed a sRec framework which basically provides random process models in continuously time spaced interval for the creation of users and topics of interest, and track the evolution of their interests. The sRec is a variation of the Bayesian approach which is computationally efficient and permits instantaneous online inference. The experimental results demonstrate the strengths of the proposed model over the state-of-the-art methods. [64] proposed a technique for real-time personalization that meets two user requirements, i.e., search ranking and home listing recommendations using Listing and User Embedding models.