Iterative Framework and Privacy Preservation in Reciprocal Recommendation.
: Although there are many reciprocal recommenders based on different strategies which have found applications in different domains but in this paper we aim to design a common framework for both symmetric as well as asymmetric reciprocal recommendation systems (in Indian context), namely Job recommendation (asymmetric) and Online Indian matrimonial system (symmetric).The contributions of this paper is multifold: i) Iterative framework for Reciprocal Recommendation for symmetric as well as asymmetric systems. ii) Useful information extracted from explicit as well as implicit sources which were not explored in the existing system (Free-text mining in Indian Matchmaking System). iii) Considered job-seekers’ personal information like his marital status, kids, current location for suggesting recommendation. These parameters are very important from practical viewpoint of a user, how he perceives a job opening. iv) Proposed Privacy preservation in the proposed framework for Reciprocal Recommendation system. We have achieved improved efficiency through our framework as compared to the existing system.