An Approach for Item Recommendation Using Deep Neural Network Combined with the Bayesian Personalized Ranking

Bi, Zhongqin; Zhou, Siming; Yang, Xiaoxian; Zhou, Ping; Wu, Jiale

doi:10.1007/978-3-030-30146-0_11

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the research field of RSs has expanded in various fields, there is a steady interest in the application of RSs to SDN environments. In this context, a predictive, automated network management recommendation was also introduced with surprising results in [22]. The main driver of these recommendations was the appropriate trading policies to enforce the recommendation module and to propel the actions required to get the best value per VNF.…”

Section: Related Workmentioning

confidence: 99%

Test Recommendation for Service Validation in 5G Networks

Touloupos

Kapassa

Kyriazis

et al. 2020

Information Systems

View full text Add to dashboard Cite

System Testing is the testing of a fully integrated software, as well as a series of different tests whose purpose is to exercise the full computer-based system. Software testing though, is an arduous and expensive activity. In the context of manual testing, any effort to reduce the test execution time and to increase defect findings is welcome. One of the biggest challenges in the upcoming 5G era is the validation and verification of VNFs and NSs, so that operators can be sure of their behavior in different execution environments. 5G technologies as well as SDNs are already introducing great challenges in the field service validation, testing and optimization of the underlying network. Considering the above, we propose a recommendation system acting as a Decision Support Mechanism integrated in an SDN environment. Its purpose is the proposition of tests to the software developers towards a stable NS ready to be deployed to the production environment.

show abstract

Section: Related Workmentioning

confidence: 99%

Test Recommendation for Service Validation in 5G Networks

Touloupos

Kapassa

Kyriazis

et al. 2020

Information Systems

View full text Add to dashboard Cite

show abstract

“…To make predictions of personalized ranked lists on items for recommendation, matric factorization captures users' and items' low-dimensional space through ratings interaction matrix. Whereas, the method only using the explicit ratings data is insufficient to make a good top-n recommendation [4][5][6]. Many existing methods take the implicit feedback into consideration for a better performance [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

DCT: A Deep Collaborative Filtering Approach Based on Content-Text Fused for Recommender Systems

Zhang

Wen

Zhou

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Recommender systems commonly make recommendations by means of user-item interaction ratings. One of the basic methodologies of recommendation is collaborative filtering, which exploits users' and items' latent space features to make predictions of personalized ranking list for an individual user. However, most of existing collaborative filtering approaches only employ explicit interaction ratings data to predict user preferences, and neglect the necessary of exploiting implicit feedback data and auxiliary information in promoting the performance recommendation. In this paper, we raise a novel model of recommendation on the basis of neural networks architecture. Concretely, the model exploits both of interaction data and content text information as input and adopts two parallel neural networks to learn the latent feature representations of users and items for a better performance. We utilize three kinds of real-world data to make extensive evaluations on the model. The experimental results reveal that the method we proposed dramatically outperforms the state-of-the-art methodologies and achieves expressively improvement in performance.

show abstract

“…Several studies have been using BPR in the recommendation of items from implicit feedback datasets. [45] presented a deep neural network model based on Stack Denoising Auto-Encoder and BPR. [46] proposed a social distance-aware BPR model for social network recommendations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets

Barros

Moitinho

Couto

2020

Preprint

View full text Add to dashboard Cite

The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the use of Recommender Systems in the selection of compounds of interest to scientific researchers. Our approach consists of a Hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, based on the semantic similarity of the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than ten percentage points in most of the assessed evaluation metrics.

show abstract

An Approach for Item Recommendation Using Deep Neural Network Combined with the Bayesian Personalized Ranking

Cited by 4 publications

References 20 publications

Test Recommendation for Service Validation in 5G Networks

Test Recommendation for Service Validation in 5G Networks

DCT: A Deep Collaborative Filtering Approach Based on Content-Text Fused for Recommender Systems

Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets

Contact Info

Product

Resources

About