New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering

Gohari, Faezeh Sadat; Tarokh, Mohammad Jafar

doi:10.1111/coin.12066

Cited by 11 publications

(8 citation statements)

References 57 publications

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“…The first, when calculating the similarity between users and resources, using semantic knowledge to describe users' interests and the contents of resources to predict users' ratings of unrated items with higher quality. Gohari and Tarokh (2016) got the semantic similarity between users and resources based on ontology and predicted the evaluation of resources that users have not evaluated based on those semantic similarities. D'Addio et al (2018) developed resource feature vectors based on sentiment analysis, semantic embedding and semantic similarity and then applied CF based on these vectors.…”

Section: Related Workmentioning

confidence: 99%

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Zhou

Yang

et al. 2021

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PurposeThis paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.Design/methodology/approachFirst, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.FindingsFolksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

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Section: Related Workmentioning

confidence: 99%

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Zhou

Yang

et al. 2021

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“…It is also used in entity and relationship searching in building ontology [33]. Reference [34] proposed a method based on reinforcement learning and semantic fusion, which was used to give suggestions for decision-making. In [35], the framework based on reinforcement learning and humanin-the-loop is proposed for driving Decision-Maker optimization.…”

Section: E Machine Learning For Data Futionmentioning

confidence: 99%

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Chen

Wang

et al. 2020

IEEE Access

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With the development of industrial Internet and artificial intelligence, data fusion in crossdomains and cross-layers have become an inevitable trend. Most of the data fusion involved in the production process of hot rolling are concentrated on the level of sensors, Internet of Things (IoT) and the Internet; but human data are not well integrated. In order to avoid the human factor from becoming the bottleneck of the entire production schedule, this paper proposes a ternary data fusion model based on reinforcement learning algorithm. The related data source from human-cyber-physical space includes: social network, Internet and IoT. By merging the ternary data, a variety of data (including humans') can be quickly calculated to obtain better and faster decisions. In order to achieve automated fusion from ternary data, this paper proposes a method based on reinforcement learning: firstly, the domain ontology used for associating ternary data is reduced and tessellated (dimension reduction), and then the reinforcement learning model is used to form "the new ontology". Compared with resource-intensive global calculations (which may cost a few days), the new method can complete the calculations in minutes. This means that the new method optimizes the data source required for decision-making and improves the efficiency. Finally, the production scheduling of hot rolled steel is used as an example to verify the feasibility of the proposed method. INDEX TERMS human-cyber-physical data fusion, ternary data fusion, cyber-physical system, domain ontology, reinforcement learning

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“…To solve such problems, recent recommender systems have focused on the integration of additional information, thus, allowing recommender systems to exploit the added information as a supplementary to the insufficient users' ratings to generate more accurate recommendations. Examples of such additional information are: the semantic relationships that are exist among users or items [10][11][12][13][14]; and the multi-criteria ratings which can imply more complex users' preferences [7,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Semantic information associated with users or items can be represented by taxonomies or ontologies and has an important task, by including concepts and their relationships, in accurately representing the item information and the user model [10][11][12][13][14]. In addition, recent studies acknowledge that multi-criteria ratings of users can be utilized to find the actual correlations between users as it based on more than one criterion [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems

Shambour¹,

Hourani²,

Fraihat³

2016

ijacsa

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Abstract-Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items' semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based MultiCriteria Collaborative Filtering algorithm that integrates the items' semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques. According to the experimental results, the proposed algorithm prove to be very effective in terms of dealing with both of the sparsity and new item problems and therefore produce more accurate recommendations when compared to standard itembased CF techniques.

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New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering

Cited by 11 publications

References 57 publications

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems

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