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Using Sequential Multi-Behavior Prod...

Bandreddy, Saadhika.

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Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation./
Author:	Bandreddy, Saadhika.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	107 p.
Notes:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31141961
ISBN:	9798381996401

Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation.
Bandreddy, Saadhika.

Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 107 p.

Source: Masters Abstracts International, Volume: 85-10.

Thesis (M.Sc.)--University of Windsor (Canada), 2024.

In most real-world recommender systems, users interact with items sequentially and multi-behaviorally. There are various user multi-behaviors in practical scenarios, such as clicks, likes, add-to-cart, and purchases. Analyzing the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the performance of recommender systems. Existing methods, such as HSPRec19, DACBRec21, and MBHT22, use customer multi-behaviour information to improve the accuracy of recommendations. HPCRec18 system used purchase frequency and consequential bond between clicks and purchased items data to improve the user-item frequency matrix. HSPRec19 system enhances the user-item rating matrix input to collaborative filtering with sequential purchase patterns by reducing the matrix sparsity. Still, it does not capture the item-level multi-behavior dependencies to further alleviate the data sparsity problems. DCABRec21 system uses multiple user behaviors and negative feedback in the Collaborative Filtering (CF) method. MBHT22 systems is a multi-behavior recommendation system that uses a hypergraph-transformer. This thesis proposes a system called the Multi-Behaviour Sequential Pattern Recommendation System (MBSPRec System), which is an extension of the HSPRec19 system that includes multi-behavior frequent patterns along with frequent click and purchase patterns to improve the accuracy of recommendations and reduce user-item rating data sparsity problem to a larger extent. The proposed MBSPRec generates a Multi-Behaviour Sequential Database for each user behavior type using the Multi-Behaviour Sequential Database Generator (MBSDBG) and Multi-Behaviour Sequential Pattern Miner (MBSPM), which mines multiple user behavior sequential pattern rules to yield additional sequential patterns and further reduce data sparsity of User-Item Matrix and improve the accuracy of the recommendations. The proposed MBSPRec mines approximate sequential data using the ApproxMAP algorithm to improve the Consequential Bond between multiple behavior and purchase sequences to give multi-behavior frequent sequential rules where no purchase has happened. Experimental results show that the proposed MBSPRec achieves more recommendation accuracy and reduces user-item rating data sparsity than the tested existing systems.

ISBN: 9798381996401Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Collaborative filtering

Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation.
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245 1 0 $a Using Sequential Multi-Behavior Product Features for E-Commerce Recommendation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 107 p.
500 $a Source: Masters Abstracts International, Volume: 85-10.
500 $a Advisor: Ezeife, C.
502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2024.
520 $a In most real-world recommender systems, users interact with items sequentially and multi-behaviorally. There are various user multi-behaviors in practical scenarios, such as clicks, likes, add-to-cart, and purchases. Analyzing the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the performance of recommender systems. Existing methods, such as HSPRec19, DACBRec21, and MBHT22, use customer multi-behaviour information to improve the accuracy of recommendations. HPCRec18 system used purchase frequency and consequential bond between clicks and purchased items data to improve the user-item frequency matrix. HSPRec19 system enhances the user-item rating matrix input to collaborative filtering with sequential purchase patterns by reducing the matrix sparsity. Still, it does not capture the item-level multi-behavior dependencies to further alleviate the data sparsity problems. DCABRec21 system uses multiple user behaviors and negative feedback in the Collaborative Filtering (CF) method. MBHT22 systems is a multi-behavior recommendation system that uses a hypergraph-transformer. This thesis proposes a system called the Multi-Behaviour Sequential Pattern Recommendation System (MBSPRec System), which is an extension of the HSPRec19 system that includes multi-behavior frequent patterns along with frequent click and purchase patterns to improve the accuracy of recommendations and reduce user-item rating data sparsity problem to a larger extent. The proposed MBSPRec generates a Multi-Behaviour Sequential Database for each user behavior type using the Multi-Behaviour Sequential Database Generator (MBSDBG) and Multi-Behaviour Sequential Pattern Miner (MBSPM), which mines multiple user behavior sequential pattern rules to yield additional sequential patterns and further reduce data sparsity of User-Item Matrix and improve the accuracy of the recommendations. The proposed MBSPRec mines approximate sequential data using the ApproxMAP algorithm to improve the Consequential Bond between multiple behavior and purchase sequences to give multi-behavior frequent sequential rules where no purchase has happened. Experimental results show that the proposed MBSPRec achieves more recommendation accuracy and reduces user-item rating data sparsity than the tested existing systems.
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650 4 $a Information science. $3 554358
653 $a Collaborative filtering
653 $a Data mining
653 $a Data sparsity
653 $a E-commerce Recommender systems
653 $a Multi-behavior Recommender system
653 $a Sequential pattern mining
690 $a 0984
690 $a 0489
690 $a 0723
690 $a 0386
710 2 $a University of Windsor (Canada). $b COMPUTER SCIENCE. $3 2093677
773 0 $t Masters Abstracts International $g 85-10.
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791 $a M.Sc.
792 $a 2024
793 $a English
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