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This is comparatively a new approach than other two approaches. This method is used in those cases where both collaborative- and content-based approaches failed or cannot work properly. The situation happens when there is not enough ratings or reviews are at hand for a particular item for the recommendation process. It is generally happening for those that are hardly ever purchased like houses, cars, or financial services. The way this approach works that it extracts the client’s perception for that domain for recommending the items that will satisfy his requirements the best. The core strength or advantage is that it does not need any previous rating of that problem. By using this approach, it can overcome the cold start problem. But it has a disadvantage also that it needs experienced engineering with all its attendant difficulties to understand the item domain satisfactorily [1].

There is another approach in the recommender system known as the hybrid approach. This approach is made to overcome the limitation of both collaborative and content-based filtering approach. It combines the strength of collaborative and content-based approach they are by combining multiple recommendation algorithm’s implementations into a single recommendation system to improve the efficiency of the recommendation system which, in turn, would show better performance. The hybrid approach is generated by combining two or more algorithms. We must take care of two major points over here. First is keeping an account of the recommendation models that declare the required inputs and the determination of the hybrid recommender system. The second point is determining the strategy that will be used within the hybrid recommender. But there are also certain demerits prevailing in this hybrid approach like it not cost-effective, i.e., it is very expensive to implement because it is an amalgamation of other filtering methods. Moreover, it increases the complexity and, sometimes, needs outside data which is unavailable most of the time [1, 18].