A bio-inspired perspective towards retail recommender system: Investigating optimization in retail inventory

Abstract

The complexity of business and different variations of service providers inspire the sense of matching of the consumers with the most appropriate products and services. This specific attribute initiates the study of recommender systems, which analysis the patterns of user's interest in items or products and services to suggest personalized recommendations for all these verticals, and also to suit a user's taste and satisfaction. High quality recommendation demands perfect decision for classifying manifold options given to user against a particular query. Hence, research challenge remains that whether the decision taken is the optimal at the end of recommended options. In this paper coined Termite Colony Optimization (TCO) is proposed, which provides a decision making model, and it is used by termites to adjust their movement trajectories under the decision tree from web service portal. We strongly advocate that the emerging TCO could be better a choice to be used in recommender system and most importantly on a continuous data stream. The present approach is tested on a brand named as “Big Bazar” (Large Market) of India. Retail recommendation has continuous data and various constraints before achieving optimized suggestions. Empirical investigations demonstrate that Termite behavior and meta-heuristic approach is quite affin to offer optimized recommendations for specifi retail operation. The research also briefs about the potential benefi of such retail recommender model in reality.