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) Considering job-seekers’ personal information like his marital status,
kids, current location for suggesting recommendation. iv) Proposed Privacy preservation in the proposed
framework for Reciprocal Recommendation system. These parameters are very important
from practical viewpoint of a user and we have achieved improved efficiency through our framework
as compared to the existing system.
Keywords: Reciprocal recommendation, privacy preserving, job recommendation, Indian matchmaking system, implicit preferences,
systematic and asystematic systems.
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