Mining High Utility Itemset for Online Ad Placement Using Particle Swarm Optimization Algorithm

Keerthi, M.; Anitha, J.; Agrawal, Shakti; Varghese, Tanya

doi:10.1007/978-3-030-37218-7_63

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…A dataset of different advertising ads shown in one site is considered in order to extract the High Utility Itemset and PSO-algorithms to mine ads with a converting rate higher than the converting rate threshold, which are used to predict types of ads leading to the generation of the higher revenue transaction data. 14 The modified binary version of gray wolf optimization (GWO) algorithm called Boolean GWO is being used to solve HUIM by five different Boolean operations: De Morgan's AND, binary adder, difference and uncertainty multiplexer. 15 A threshold of too large or too short value can result in improper high utility element sets, this makes the task of deciding the exact utility threshold a harder one in HUIM.…”

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

“…Three techniques are used: a demographic diversity preservation approach to increase the exploration space for excellent solutions while reducing the amount of lost HUIs, a neighborhood discovery strategy for repetitive HUIs, and an expert strategy to avoid the disappearance of high‐quality itemsets. A dataset of different advertising ads shown in one site is considered in order to extract the High Utility Itemset and PSO‐algorithms to mine ads with a converting rate higher than the converting rate threshold, which are used to predict types of ads leading to the generation of the higher revenue transaction data 14 . The modified binary version of gray wolf optimization (GWO) algorithm called Boolean GWO is being used to solve HUIM by five different Boolean operations: De Morgan's AND, binary adder, difference and uncertainty multiplexer 15 .…”

Section: Related Workmentioning

confidence: 99%

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Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Logeswaran¹,

Suresh²

2021

Computational Intelligence

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Mining high utility itemsets (HUI) is a current thrust field in data mining that has received numerous methodologies for addressing it effectively. The difficulty with HUI is to locate a number of items that have a high degree of utility in comparison to other different sets in a transaction database. Traditional accurate HUIM algorithms usually have to solve the exponential problem of big search spaces when the size or number of different items in the database is quite vast.

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Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Logeswaran¹,

Suresh²

2021

Computational Intelligence

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“…To extract the High Utility Itemset, the suggested approach in [6] considers a dataset of distinct advertisements that are presented on a single website. For the proposed system, the high utility itemset (HUI) are advertising through a conversion proportion greater than the predefined conversion proportion threshold value, which is utilised to anticipate the sorts of ads that generate more income.…”

Section: Review Of Literaturementioning

confidence: 99%

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Logeswaran¹,

Suresh

Anandamurugan³

2023

ITC

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Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

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“…The rule mining approach is considered as an effective method to map the relationship among the items in the dataset. A threshold based strategy is adopted in PSO algorithm which chooses the items which are higher than the threshold value (10). The itemsets from the real time transactional databases are extracted based on the maximum usage of each itemset using the maximal itemset mining algorithm.…”

Section: Related Artmentioning

confidence: 99%

Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

Pazhaniraja¹,

Sountharrajan²,

Suganya³

et al. 2018

EAI Endorsed Transactions on Energy Web

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High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the customers and cost of purchased product. This can be resolved by high utility itemset mining which includes quantities and profit of the products in the transactions. The conventional association rule mining algorithms results in huge memory consumption due to the complexity in pruning the search space. In this paper, machine learning based high-utility itemset mining is applied to predict next order in an online grocery store depending on the transactions. The overall goal is to enhance the business profitability by stocking the high utility items in market. The Top 'N' variant Random Forest model is proposed to recommend the high utility itemsets, thereby predicting the reordered/next ordered items. The model is evaluated using Instacart market dataset to measure accuracy, precision and recall.

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Mining High Utility Itemset for Online Ad Placement Using Particle Swarm Optimization Algorithm

Cited by 6 publications

References 10 publications

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

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