## 4 Results
### Temporal Models and Recommendation Approaches
#### 4.3.2 Time-Independent Models: Time as Context  
Time-independent models on the hand exploit time as an additional context that has significant effects in learning and predicting user preferences. In other words, timestamps are used as an explicit additional type of data in the model. There is a considerable body of work on context-aware RS in the literature. However, we specifically focused only on the works that dealt precisely with time, such as the time-aware neighborhood-based or time-aware factor-based models. _Time-Aware factor model_ One of the primitive work where time is explicitly used was a Pairwise Interaction Tensor Factorization (PITF) proposed by [139]. The PITF is a variant of the tensor factorization that has a linear runtime for both the learning and prediction tasks and works by jointly learning the pairwise interactions of users, items and the tags. By replacing the tag dimension with time information in the user-item-time triplet matrix, the resulting matrix is then factorized to obtain the corresponding feature models. This time-aware approach, therefore, takes advantage of temporal information to track the concept drift of user preferences which makes it performed better compared to state of the art methods without this capability. In recent contributions, [85] provided a personalized time-aware tag RS by extending the PITF model for both time awareness and the cold start problem in personalization aspects [91]. For the time-awareness aspect, the model leverage the time effect to capture the concept drift of user preferences and the most popular tags on items to handle the cold start situation of new users. Different from the power-form functions used in the existing time-aware recommendation models, the authors used a temporal weight with the exponential intensity function to improve the model's efficiency. [140] proposed a new recommendation approach that mainly focused on the identification of semantic attitudes extracted from user-generated content, such as user sentiment, volume, and objectivity. In order to achieve that, the authors devised a three-dimensional matrix factorization which also takes into consideration the temporal alterations of users' behaviors. _Time-aware Neighbourhood models_ In this approach, the time information is mostly utilized when computing the k-neighbor of users and items for the time-aware RS. For instance, [8] propose a time-aware Point-Of-Interest (POI) RS by integrating the users' check-in time in the calculation of cosine similarity between users in a traditional user-user CF method. The intuition behind this method is that users' similarity is determined based on their check-in locations and time-of-day information [4]. The users that checked-in in the same location at the same time are more similar than those that checked-in in the same location but at different times. In [141], the rating abstention interval (RAI) algorithm is used for handling concept drift in social-network RS. The idea is that, whenever a user refuses to submit ratings for a long period of time, more than a predefined interval, then the user preferences are considered invalid as a result of concept drift occurrence in user interests. In another contribution, [63] used a fuzzy c-means clustering and entropy to find the preference change in the rating timeline of users. When the preference changed points of the target user are detected across the timeline, the periods in the past with similar preference to the current preference period are used to compute his/her neighbors and predict the target item's rating. The results suggest that the approach can significantly improve recommendation accuracy. However, as the method does not necessarily use the entire user data in the past but only those that are similar to the current period to make predictions, this makes it more prone to a cold start and data sparsity challenges. _The hybrid method_ involves the integration of time-aware with time-dependent methods to derived the benefit of the two methods.