Abstract: Now a days, recommendation systems have seen outstanding evolution in the field of knowledge engineering. Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement. Still, performance of most of the existing collaborative filtering. Based recommendation system suffers due to the challenges, like: (a) cold start, (b) data sparseness, and (c) scalability. But, recommendation problem is often characterized by the presence of many conflicting objectives or decision variables, such as users’ preferences and venue closeness. In this work, introduced Cloud-based Bi-Objective Recommendation Framework (BORF) for mobile social networks. These utilizes multi-objective optimization approaches to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data pre-processing by using the Hub-Average (HA) inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for Pyramid Maintenance Algorithm (PMA) is applied for vector optimization to provide optimal suggestions to the users about a venue. The results of comprehensive experiments on a large area real dataset confirm the accuracy of the proposed recommendation framework. In the future, we would like to increase our work by incorporating more contextual data in the form of objective functions, such as the check-in time, users’ profiles, and interests in our proposed framework. Moreover, we intend to integrate other concepts, such as machine learning, text mining, and artificial neural networks to refine our existing framework.

Keywords: Context-Aware Web Services, MOPNAR, Multi-objective Optimization, Collaborative Filtering.