Context-Aware Service Recommendation Based on Knowledge Graph Embedding

Mezni, Haithem; Benslimane, Djamal; Bellatreche, Ladjel

doi:10.1109/tkde.2021.3059506

Cited by 36 publications

(10 citation statements)

References 57 publications

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“…Due to users in ASI having stable purchase interests, it assumes that their stable interests do not change over time, and the time incentive factor w α t u k i s is a periodic piecewise constant function. The model of the time incentive function is as shown in Equation (11).…”

Section: User-item Interactionmentioning

confidence: 99%

“…Existing studies have proposed many recommendation algorithms to predict users' potential interests in items by characterizing user preferences and item characteristics, e.g., the nearest neighborhood based recommendation algorithm [5][6][7], the matrix factorization based recommendation algorithm [8,9] and the context aware recommendation algorithm [10,11]. Clearly, the basic idea of these algorithms is straightforward-that if a user purchased an item in the past, he or she will also purchase similar items, or items purchased similar users at that time, in the future.…”

Section: Introductionmentioning

confidence: 99%

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ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Shi

et al. 2022

Mathematics

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Existing studies have made a great endeavor in predicting users’ potential interests in items by modeling user preferences and item characteristics. As an important indicator of users’ satisfaction and loyalty, repeat purchase behavior is a promising perspective to extract insightful information for community e-commerce. However, the repeated purchase behaviors of users have not yet been thoroughly studied. To fill in this research gap from the perspective of repeated purchase behavior and improve the process of generation of candidate recommended items this research proposed a novel approach called ReRec (Repeat purchase Recommender) for real-life applications. Specifically, the proposed ReRec approach comprises two components: the first is to model the repeat purchase behaviors of different types of users and the second is to recommend items to users based on their repeat purchase behaviors of different types. The extensive experiments are conducted on a real dataset collected from a community e-commerce platform, and the performance of our model has improved at least about 13.6% compared with the state-of-the-art techniques in recommending online items (measured by F-measure). Specifically, for active users, with w = 1 and N(UA)∈[5,25], the results of ReRec show a significant improvement (at least 50%) in recommendation. With α and σ as 0.75 and 0.2284, respectively, the proposed ReRec for unactive users is also superior to (at least 13.6%) the evaluation indicators of traditional Item CF when N(UB)∈[6, 25]. To the best of our knowledge, this paper is the first to study recommendations in community e-commerce.

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Section: User-item Interactionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Shi

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…Other benefits of semantic technology such as data interopretability, connectivity of data across domains and faster and easier knowledge discovery, have been further discussed in more detail in [ 26 , 27 , 28 , 29 , 30 ]. Because of the benefits of semantic technology, we can find its application in domains such as predictive maintenance [ 31 , 32 ] and recommender systems [ 33 ] that utilise ontologies [ 34 ] and KGs. Additionally, Kainzner et al [ 35 ] demonstrate the potential benefits of semantic technology in relevant domains such as manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Based Hard Drive Failure Prediction

Chhetri

Kurteva

Adigun

et al. 2022

Sensors

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The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.

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“…Although clothing styles are changeable, people's understanding of the concept of clothing style is not strong [ 7 ]. Therefore, the clothing recommendation service of merchants to users is significant [ 8 ]. Lots of literature research results show that although great progress has been made in the method of clothing recommendation system, the research on it is not perfect.…”

Section: Introductionmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

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Context-Aware Service Recommendation Based on Knowledge Graph Embedding

Cited by 36 publications

References 57 publications

ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Knowledge Graph Based Hard Drive Failure Prediction

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

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