December 2022

Prof. P Krishna Reddy and his students published the following papers in Springer:

• A framework for itemset placement with diversification for retail businesses – Appl. Intell. 52(12): 14541-14559 (2022)

Research work as explained by Anirban Mondal, Raghav Mittal, Parul Chaudhary and Prof. P Krishna Reddy: 

Alongside revenue maximization, retailers seek to offer a diverse range of items to facilitate sustainable revenue generation in the long run. Moreover, customers typically buy sets of items, i.e., itemsets, as opposed to individual items. Therefore, strategic placement of diversified and high-revenue itemsets is a priority for the retailer. Research efforts made towards the extraction and placement of high-revenue itemsets in retail stores do not consider the notion of diversification. Further, candidate itemsets generated using existing utility mining schemes usually explode; which can cause memory and retrieval-time issues. This work makes three key contributions. First, we propose an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification. A higher degree of diversification is indicative of fewer repetitive items in the top-revenue itemsets. Second, we propose the kUI (k U tility I temset) index for quick and efficient retrieval of diverse top-λ high-revenue itemsets. We also propose the HUDIP (H igh-U tility and D iversified I temset P lacement) scheme, which exploits our proposed kUI index for placement of high-revenue and diversified itemsets. Third, our extensive performance study with both real and synthetic datasets demonstrates the effectiveness of our proposed HUDIP scheme in efficiently determining high-revenue and diversified itemsets.

• A Pattern Mining Framework for Improving Billboard Advertising Revenue – Trans. Large Scale Data Knowl. Centered Syst. 52: 127-147 (2022)

Research work as explained by P Revanth Rathan, Prof. P Krishna Reddy and Anirban Mondal: 

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. A billboard operator manages the ad slots of a set of billboards. Normally, a user traversal is exposed to multiple billboards. Given a set of billboards, there is an opportunity to improve the revenue of the billboard operator by satisfying the advertising demands of an increased number of clients and ensuring that a user gets exposed to different ads on the billboards during the traversal. In this paper, we propose a framework to improve the revenue of the billboard operator by employing transactional modeling in conjunction with pattern mining. Our main contributions are three-fold. First, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue. Second, we propose an efficient user trajectory-based transactional framework using coverage pattern mining for improving the revenue of the billboard operator. Third, we conduct a performance study with a real dataset to demonstrate the effectiveness of our proposed framework.