Context- Aware Recommender Systems (CARS)

Tailored services or information for mobile users became very crucial; due to the enormous amount of information and services that are available for mobile users during their various activities. Recommendation system can be merged with context awareness to provide tailored services, information, moreover learning resources for users, based on their current context and profile. Context Aware Recommendation Systems (CARSs) have various usages also many applications and services depend on it, CARSs nowadays become widely used in various domains. This paper presents a survey of CARSs overview, frameworks, techniques, algorithms, and applications. Moreover, the paper discusses the issues of CARSs in general and investigates the proposed solutions.

To follow the dynamicity of the user's content, researchers
have recently started to model interactions between users and the Context-
Aware Recommender Systems (CARS) as a bandit problem where the
system needs to deal with exploration and exploitation dilemma. In
this sense, we propose to study the freshness of the user's content in
CARS through the bandit problem. We introduce in this paper an algo-
rithm named Freshness-Aware Thompson Sampling (FA-TS) that man-
ages the recommendation of fresh document according to the user's risk
of the situation. The intensive evaluation and the detailed analysis of the
experimental results reveals several important discoveries in the explo-
ration/exploitation (exr/exp) behaviour