On the benefit of logic-based machine learning to learn pairwise comparisons

Qomariyah, Nunung Nurul; Kazakov, Dimitar; Fajar, Ahmad Nurul

doi:10.11591/eei.v9i6.2384

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Machine learning and deep learning techniques were used to develop RS as in the cases of [51], [30], [33], [40], [35], [37], [21], [42] who proposed LARS with several machine learning models such as Neural Networks, K-nearest Neighbor, and associative data mining algorithms. It was reported by [60] that majority of works in recommender systems use a form of statistics or machine learning to predict the most favorites items to customers. Despite the success rate of this approaches which have proven to be accurate in many domains, the approaches suffer from a major drawback of, it can only work well with a large volume of data.…”

Section: Machine Learning (Ml-lars)mentioning

confidence: 99%

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Haruna¹,

Musa²,

Yunusa³

et al. 2022

SITech

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Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.

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Section: Machine Learning (Ml-lars)mentioning

confidence: 99%

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Haruna¹,

Musa²,

Yunusa³

et al. 2022

SITech

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show abstract

“…Recommender systems offer products or services according to the users' preferences [25] by utilizing common data such as ratings, reviews, and feedback [26]- [28] to generate personalized recommendations [29], [30]. Recommender systems can be classified into several types based on the data used to generate recommendations.…”

Section: Introductionmentioning

confidence: 99%

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

2022

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.

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“…A user may also feels difficult to give a slightly lower preference to some items, because the rating scale does not normally have a half score. Therefore, there is another approach, called pairwise elicitation, which has been introduced by the researchers in this domain which shows pair choices to the users, such as in [6,7,8,9,10,11,12,13]. By using this approach, a user will be shown with a series of pair options and a preference can be expressed by selecting only one of the most preferred item between the two items (a pair) at a time.…”

Section: Introductionmentioning

confidence: 99%

A Genetic-Based Pairwise trip Planner Recommender System

Qomariyah

Kazakov

2021

Preprint

Self Cite

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The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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On the benefit of logic-based machine learning to learn pairwise comparisons

Cited by 5 publications

References 18 publications

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

A Genetic-Based Pairwise trip Planner Recommender System

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