Towards retail market recommendations using Termite Colony Optimization

Internet as a tool of transferring data helps more industries to be more effective in their specialization. E-commerce as the next step after internet used for selling and buying. Due to the vast amount of data in this process which makes it hard and time consuming. Therefore recommender system has been introduced. It attempts to predict items or products that a user may be interested in. However, despite all the efforts, recommender systems are still in need of further development and more advanced recommendation modeling methods as these systems must take into account additional requirements on user preferences. An important contribution of this paper is TCO (Termite Colony Optimization) approach to recommender systems, the proposed approach provides a decision making model which is used by termites to adjust their movement trajectories. This approach has been tested on Large Market data and also on MovieLens data. Retail recommendation has continuous data and various constraints before achieving optimized suggestions. Empirical investigations demonstrate that Termite behavior and metaheuristic approach is quite affine to offer optimized recommendations for specific retail operation.