Abstract:- Recommendation systems have seen major evolution
in the field of information engineering. Many of existing
recommendation systems built their copies on collaborative
filtering approaches which create them easy to appliance.
performance of most of the existing collaborative filtering-based
recommendation system suffers because of many challenges,
occurrence of some incompatible objectives or decision
variables, such as users’ preferences and venue closeness. In
this paper, we estimated MobiContext, a hybrid cloud-based Bi-
Objective Recommendation Framework (BORF) for mobile social
networks. The MobiContext uses multi- objective optimization
techniques for generating personalized recommendations. To tackle
the problems affecting to cold start and data sparseness, the BORF
accomplishes d a t a p r e p r o c e s s i n g u s i n g the Hub-
Average (HA) inference model. the Weighted Sum Approach
(WSA) is employed for scalar optimization and an evolutionary
algorithm (NSGA-II) is applied for vector optimization for
providing greatest suggestions to the users about a venue. The
outcomes of complete experiments on a large-scale real dataset
verify the correctness of the planned recommendation framework.
Keyword— Multi-objective optimization, Collaborative Filtering
(CF), Non-dominated Sorting Genetic Algorithm (NSGA-II).