MobiContext: A Context-aware Cloud-Based Venue Recommendation Framework (Preprint-2017)
The main aim of this project is to recommendation systems have seen significant evolution in the field of knowledge engineering.
We proposed a cloud-based framework MobiContext that produces optimized recommendations by simultaneously considering the trade-offs among real-world physical factors, such as person’s geographical location and location closeness. The significance and novelty of the proposed framework is the adaptation of collaborative filtering and bi-objective optimization approaches, such as scalar and vector. In our proposed approach, data sparseness issue is addressed by integrating the user-to-user similarity computation with confidence measure that quantifies the amount of similar interest indicated by the two users in the venues commonly visited by both of them. Moreover, a solution to cold start issue is discussed by introducing the HA inference model that assigns ranking to the users and has a precompiled set of popular unvisited venues that can be recommended to the new user.