A novel matrix factorization model for recommendation with LOD-based semantic similarity measure

Wang, Ruiqin; Cheng, Hsing Kenneth; Jiang, Yunliang; Lou, Jungang

doi:10.1016/j.eswa.2019.01.036

Cited by 54 publications

(22 citation statements)

References 7 publications

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“…In particular, the User Splitting technique is used to break down user ratings based on the user's contextual information Wang et al 2019) to the tensor case. In this way, it is obtained a strategy of the introduction of contextual information Modelling.…”

Section: Collaborative-filteringmentioning

confidence: 99%

Context-aware recommender systems and cultural heritage: a survey

Casillo

Colace

Conte

et al. 2021

J Ambient Intell Human Comput

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In the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

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Section: Collaborative-filteringmentioning

confidence: 99%

Context-aware recommender systems and cultural heritage: a survey

Casillo

Colace

Conte

et al. 2021

J Ambient Intell Human Comput

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“…This section provides a comprehensive comparison with recent studies 30‐42 related to our approach. This comparison considers the underlying technology, including open data, machine learning, semantic web, and cloud computing, as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…The GoDaaS still lacks intelligence, such as machine learning or semantic web technologies, to process the OGD. Several studies 32‐34,36,41 have focused on using machine learning technologies, including supervised learning, unsupervised learning, bayesian network, to combine OD to develop an intelligent application, such as recommender system and decision system. By contrast, some studies 30,32,34,35,38,41,42 focus on adopting semantic web technologies, including RDF, RDF schema, OWL, associated with OD to provide specific domain knowledge and promote intelligence of application.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies 32‐34,36,41 have focused on using machine learning technologies, including supervised learning, unsupervised learning, bayesian network, to combine OD to develop an intelligent application, such as recommender system and decision system. By contrast, some studies 30,32,34,35,38,41,42 focus on adopting semantic web technologies, including RDF, RDF schema, OWL, associated with OD to provide specific domain knowledge and promote intelligence of application. Other significant researches 31‐33,37,39,40 try to use cloud computing, such as Spark, Hadoop, and edge computing, to improve performance.…”

Section: Related Workmentioning

confidence: 99%

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Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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Summary Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.

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“…Del Corso et al [6] built an NMF model that change adaptively the function to be minimized at each step. Wang et al [23] proposed an MF model named LOD-MF, which dug out implicit feedback information and applied a hybrid similarity measure to identify the semantically similar neighbors of the target item. Cai et al [2] introduced a novel parameter tuning framework named mrMoulder.…”

Section: Related Workmentioning

confidence: 99%

AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Zheng

2019

ITC

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Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

show abstract

A novel matrix factorization model for recommendation with LOD-based semantic similarity measure

Cited by 54 publications

References 7 publications

Context-aware recommender systems and cultural heritage: a survey

Context-aware recommender systems and cultural heritage: a survey

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

AANMF: Attribute-Aware Attentional Neural Matrix Factorization

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