Discovering Periodic High Utility Itemsets in a Discrete Sequence

Fournier‐Viger, Philippe; Wu, Youxi; Dinh, Duy-Tai; Song, Wei; Lin, Jerry Chun‐Wei

doi:10.1007/978-981-16-3964-7_8

Cited by 6 publications

(2 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utility of an item i in a transaction database is termed as u(i, ), u(i, ) ( ) ( ) ( ) where p(i) is the predefined external utility or profit unit of item i. The internal utility include the frequency or quantity of item i in is indicated as q(i, ) and the stability of item i in is measured using the maximum periodicity function [32] and indicated as s(i, ). As a result, the item's importance is calculated by dividing the sum of its importance or utility according to these criteria by the number of criteria.…”

Section: The Proposed Hybrid Recommendation Techniquementioning

confidence: 99%

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

fouad¹,

Hussein

Rady

et al. 2022

IJICIS

View full text Add to dashboard Cite

Based on a variety of information sources, recommender systems can identify specific items for various user interests. Techniques for recommender systems are classified into two types: personalized and non-personalized. Personalized algorithms are based on individual user preferences or collaborative filtering data; as the system learns more about the user, the recommendations will become more satisfying. They do, however, suffer from data sparsity and cold start issues. On the other hand, non-personalized algorithms make recommendations based on the importance of the items in the database; they are very useful when the system has no information about a specific user. Their accuracy, however, is limited by the issue of personalization. In most cases, one of the recommendation categories can be used to make recommendations. Yet, it is a challenge to evaluate the importance of items to the user while simultaneously using personalized and non-personalized preferences functions and ranking a set of candidate items based on these functions. This paper addresses this issue and improves recommendation quality by introducing a new hybrid recommendation technique. The proposed hybrid recommendation technique combines the importance of items to the user obtained by the utility mining method with the similarity weights of items produced by the collaborative filtering technique to make the recommendation process more reasonable and accurate. This technique can provide appropriate recommendations whether or not users have previous purchasing histories. Finally, experimental results show that the proposed hybrid recommendation technique outperforms both implemented collaborative filtering and utility-based recommendation techniques.

show abstract

Section: The Proposed Hybrid Recommendation Techniquementioning

confidence: 99%

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

fouad¹,

Hussein

Rady

et al. 2022

IJICIS

View full text Add to dashboard Cite

show abstract

“…The topic of FSM has been extended to high utility sequence mining to overcome that constraint. This latter issue is designed to manage sequences of internal and external utility values annotated in objects as mentioned [26].…”

Section: High Utility Itemset Mining Algorithmsmentioning

confidence: 99%

Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket

NELLUTLA>

Srinivasan²

2022

ECJSE

View full text Add to dashboard Cite

High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most of the measurements are expected. In real time applications, such as the market basket review, company decision making and web administration organization results, large quantities of datasets are slowly evolving with new knowledge incorporated. The usual mining calculations cannot handle such complex databases and retrieve useful data. The essential task of data collection in a quantifiable sequence dataset is to determine entirely high utility sequences. The number of sequences found is always extremely high, though useful. This article studies the issue of the mining of repeated high utility sequence that meet item restrictions in order to identify patents that are more suited to the needs of a customer. Also, this article introduces high-value element set mining, examines modern algorithms, their extensions, implementations, and explores research opportunities.

show abstract

IPHM: Incremental Periodic High-Utility Mining Algorithm in Dynamic and Evolving Data Environments

Huang,

Chen,

Chen

2024

Heliyon

View full text Add to dashboard Cite

Discovering Periodic High Utility Itemsets in a Discrete Sequence

Cited by 6 publications

References 68 publications

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket

IPHM: Incremental Periodic High-Utility Mining Algorithm in Dynamic and Evolving Data Environments

Contact Info

Product

Resources

About