Abstract
The most explored goal of mining association rules between items is to identify all possible groups of items which are used together by a set of users for purchasing, selling or any other purpose. But, it is also important to mine the association rule between merchants to get the groups of brands and item types which are used together. In this paper, we wish to find the inter-merchant association rules in the transaction database based on the distance between the merchants, the categories of the merchants, the number of times their items are purchased together, and the preferences of the users. The task is implemented in three phases. In the first phase, we preprocess the transactions by removing the unnecessary transactions and grouping them based on related users and time frames. In this phase, we have also mapped the store name and the owner name to the corresponding merchant name. In the second phase, we find top T number of merchants from each category using Association Rule Mining. Then, in the third phase we display the recommendations in the decreasing order of the distance from the last merchant. This work may be used to provide appropriate recommendations of merchants to the intended users, increase the user engagement in transactions and in turn increase the sell of the products of a merchant. The system is trained on a dataset of total 50,000 transactions of 5 users and tested for 5 users for getting the rules meant for them. The proposed system gives 83.3% correct results. All the results are useful as manually evaluated by the users.
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Acknowledgements
The author sincerely acknowledges the efforts of a couple of the Samsung developers Mr. Bagadhi Gopal Rao and Mr. Karan Chauhan and a couple of Samsung reviewers Mr. Ramachandran Narasimhamurthy and Mr. Nitish Varshney for their effective helps.
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Chatterji, S. Recommending the Best Merchant from Previous Transactions. Mobile Netw Appl 24, 202–207 (2019). https://doi.org/10.1007/s11036-018-1053-8
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DOI: https://doi.org/10.1007/s11036-018-1053-8