## 2 Theoretical Background
### Types of Concept Drifts Incorporated in DRSs  
Research on concept drifts in RS is motivated following the success of [5] in the Netflix movie competition held on the 21 st September 2009. The team considered three sources of drift aspects as key factors and combined multiple base learners to improve accuracy. These include user biases (i.e., users' rating scales natural drift), item biases (i.e., item's popularity changes after some times) and changes in user preferences. Following this development, other several drift aspects were identified some of which we reviewed and discussed as follows:  
_A. Customer Preferences Changes:_ A Customer may change his or her preferences on items over a period of time for one reason or another [22]. For example, when a family structure changes, it may lead to changes in customer's shopping styles. Similarly, a young boy who likes cartoon films at a young age may enjoy watching action films as an adult. Therefore, disregarding these changes when modeling the preferences of a user might result in building unhelpful recommendations [28]. _B. Change of Product Perceptions and Popularities:_ The item's perceptions and popularity may change over time which has a great impact on the focus of customers [29]. An item may gain popularity when the external event is triggered, or when a popular actor appears in a new film, and it loses its popularity when it is too old [9; 10]. _C. Item fundamental properties._ Although more concern is given to the above drifting concepts, change in item features have an equal chance of occurrence as user behaviors or item popularity change with respect to time [12]. For instance, in a mobile phone recommendation domain, the phone's hardware features such their interfaces, sizes, capabilities, services and applications can change over a time-space. These changes, however, can influence the mobile phone operators to redefine their interest as new phones emerge [30]. _D. Dynamic Interest within the Community._ Two users with similar tastes at a time may tend to have different tastes in another time. Likewise, the interest of the whole community changes with time [31]. The goal here will be to detect such changes among the subgroups of users and to adapt the learning models to such changes to ensure accurate recommendations. _E. Seasonal Changes._ The changes from season to season or certain holidays may give rise to different ways of users' shopping styles [5]. Some items in certain seasons are equally more popular than others. In a like manner, users may also have different buying patterns during the weekends, holidays and seasons compare to working days. The goal is to provide for users the kinds of products or services that suit their needs at a particular time of the year such as spring, summer, autumn or winter [32]. _F. User-Item Bias Shifting._ Bias occurs when some users are more inclined to give higher rating values compare to others who tend to give lower ratings, or when some items receive high rating values compare to others [33]. For instance, a user who tends to give an average movie 4-stars ratings may be inclined to now give such movie a 3-stars rating. Different users in the events of multi-person accounts also tend to give different ratings for similar items [5; 33]. Other factors include the ratings given in relation to other ratings previously given by others. G. Transient or short-term changesTransient effects are usually associated with changes in the shortest possible time such as single-day effects or a session. A good instance of this was established in [5; 34; 35] where it was noted in the movielens dataset that, single day ratings given by a user tend to concentrate around a single value, which may possibly be attributed to user' mood for that particular day. Such effects thus do not last for more than a whole day. H. _Dynamic point of interest_. In Location-Based RS, users check-in behaviors tend to change over time while exploiting several Points-of-Interests such as clubs, cinemas or restaurants.