Abstract: Now a days, recommendation systems have seen significant evolution in the field of knowledge engineering. The models of most of the existing recommendation systems based on collaborative filtering approaches that make them simple to implement. The challenges that affect the performance of most of the existing collaborative filtering- based recommendation system are (a) cold start, (b) data sparseness, and (c) scalability. In this paper, introduced Cloud-based Bi-Objective Recommendation Framework for mobile social networks. Multi-objective optimization techniques are used to generate personalized recommendations. Hub-Average (HA) inference model is used to address the issues pertaining to cold start. The Weighted Sum Approach (WSA) is implemented for CF-BORF and greedy-BORF Algorithm is applied for vector optimization to provide optimal suggestions to the users about a venue.
Keywords: Context-Aware Web Services, Multi-objective Optimization, Collaborative Filtering.