BizSeeker

Lu, Jie; Shambour, Qusai; Xu, Yisi; Lin, Qing; Zhang, Guangquan

doi:10.1108/10662241011050740

Cited by 72 publications

(5 citation statements)

References 28 publications

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“…(Moreno et al, 2013). Some other researchers have implemented the semantic recommendation by combining the item-based semantic similarity and item-based ColF method (Lu et al, 2010). Daramola et al (2009) introduced an e-tourism recommender system that uses ontology for tourism purposes.…”

Section: Ontology In Recommender Systemsmentioning

confidence: 99%

A hybrid semantic recommender system based on an improved clustering

Bahrani,

Minaei-Bidgoli,

Parvin

et al. 2024

J Supercomput

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A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users' interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should deal with. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to deal with the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings' prediction. If our target client is severely sparse, we can not trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems.

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Section: Ontology In Recommender Systemsmentioning

confidence: 99%

A hybrid semantic recommender system based on an improved clustering

Bahrani,

Minaei-Bidgoli,

Parvin

et al. 2024

J Supercomput

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“…Leveraging Twitter activities for user profile modeling to recommend followees with gainful information, in that context, many studies were dedicated to profiling modeling. Abel et al (2011) introduced a framework for user modeling on Twitter that enriches the semantics of tweets and identifies topics and entities mentioned in tweets, Also Lu et al (2010) presented a “hybrid government-to-business e-services” semantic recommendation system. They discussed strategies for constructing hashtag-based, entity-based and topic-based user profiles that benefit from semantic enrichment and explore the profiles’ temporal dynamics.…”

Section: Related Workmentioning

confidence: 99%

Incorporating LDA with LSTM for followee recommendation on Twitter network

Dib

Kalloubi

Nfaoui

et al. 2021

IJWIS

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Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

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“…Xu, Lin and Zhang, 2010;Parasuraman, Zeithaml and Malhotra, 2005;Sousa and Voss, 2006). Zeithaml et al (2002) mentioned if e-service quality as the extent to which a website provides shopping, purchasing and shipping facilities effectively and ef iciently.…”

Section: Literature Reviewmentioning

confidence: 99%

The Mediating Role of Satisfaction in the Relationship between Service Quality and Loyalty: Case Study in PT Asuransi XYZ Indonesia.

Vasconcellos

Pignanelli

Neto

2020

IJBS

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The objective of this study is to analyze the relationship of service quality on agents’ satisfaction, service quality on agents’ loyalty, and satisfaction on loyalty when they used the insurance portal XYZNet as a digital tools provided by PT Asuransi XYZ Indonesia for their agents’ to increase market share in retail business in Indonesia. Where the portal was facing the challenge of the low number of usage. This study used a quantitative approach, involved 100 respondents which have minimum five times using this portal as samples. The analysis technique used in this study is Partial Least Square (PLS) Structural Equation Modeling (SEM), with the help of SmartPLS version 3.2.8 software. The results indicated that service quality positively influenced agents’ satisfaction, satisfaction also positively influenced agents’ loyalty. However service quality did not significantly influenced agents’ loyalty. The finding also indicated that satisfaction was a significant variable that mediated the relationship between service quality and loyalty. The study gives positive impact to the future researchers to do similar study. Furthermore, the finding could help PT Asuransi XYZ Indonesia and insurance company in general to improve their service quality.

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BizSeeker

Cited by 72 publications

References 28 publications

A hybrid semantic recommender system based on an improved clustering

A hybrid semantic recommender system based on an improved clustering

Incorporating LDA with LSTM for followee recommendation on Twitter network

The Mediating Role of Satisfaction in the Relationship between Service Quality and Loyalty: Case Study in PT Asuransi XYZ Indonesia.

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