A Content-Based Recommendation System Using Neuro-Fuzzy Approach

Rutkowski, Tomasz M.; Romanowski, Jakub; Woldan, Piotr; Staszewski, Paweł; Nielek, Radosław; Rutkowski, Leszek

doi:10.1109/fuzz-ieee.2018.8491543

Cited by 43 publications

(9 citation statements)

References 14 publications

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“…Our experiments are intended to (1) validate the rationality of our proposed approach; (2) compare our approach with other trajectory prediction methods; (3) compare our approach with other recommendation methods without considering the mobility; and (4) analyze parameters of our approach to achieve optimum performance. (14) rs,u,i = × ru,i + (1 − ) × rs,i ,…”

Section: Resultsmentioning

confidence: 99%

“…Many literature over the past decade focused on developing service recommendation systems. Most of these works focus on collaborative filtering based recommendation [7,12,13], content-based recommendation [14,15] and model-based recommendation [16,17]. Shao et al [18] proposed a user-based collaborative filtering method for predicting the web service QoS value and conducted experiments on 20 web services.…”

Section: Related Workmentioning

confidence: 99%

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Mobility-aware personalized service recommendation in mobile edge computing

Hong-xia

Dong

Yang

2021

J Wireless Com Network

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With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mobility-aware personalized service recommendation in mobile edge computing

Hong-xia

Dong

Yang

2021

J Wireless Com Network

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“…Many literature over the past decade focused on developing service recommendation systems. Most of these works focus on collaborative filtering based recommendation [6,9,10], content-based recommendation [11] and model-based recommendation [12,13].…”

Section: Related Workmentioning

confidence: 99%

“…where U denotes the set of similar users to user u, who have invoked service i; nr u ,i is the value of service i from user u in the row-normal matrix U S nu ; r umin and r umax are the lowest and the highest values from user u in the original matrix U S, respectively; and Sim(u, u ) can be computed by formula (11). In the server-based value prediction, we first cluster servers based on K-means to reduce the influence of the servers sparsity.…”

Section: Location-based Collaborative Filteringmentioning

confidence: 99%

Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

Hong-xia

Dong

Yang

2020

Preprint

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With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users\' mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation approach based on user trajectory and QoS predictions. In the proposed method, users' trajectory is firstly discovered by hybrid long-short memory networks. Then, given users\' trajectories, service QoSs are predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy based on the aforementioned information. Experimental results based on the real base station dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of the accuracy in mobile edge computing.

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“…RS is a great machine learning system to increase product sales [ 6 , 7 ]. Recommendation helps the user to speed up the search process and makes it simple for them to obtain content that is interesting to them, as well as provide them with offers they would not have searched for [ 8 , 9 ]. Furthermore, companies may attract customers by showing movies and TV shows relevant to their profiles [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

Kumar

Singh

Jain

et al. 2022

Computational Intelligence and Neuroscience

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Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they recommend a new item. A novel deep ranking weighted multihash recommender (DRWMR) system is designed to suppress sparsity and CSP. The proposed DRWMR system contains two stages: the neighbours’ formation and recommendation phases. Initially, the data is fed to the deep convolutional neural network (CNN). The significant features are extracted from CNN. The CNN contains an additional layer; the hash code is generated by minimising pairwise ranking loss and classification loss. Therefore, a weight is assigned to different hash tables and hash bits for a recommendation. Then, the similarity between users is obtained based on the weighted hammering distance; the similarity between users helps to form the neighbourhood for the active user. Finally, the rating for unknown items can be obtained by taking the weighted average rating of the neighbourhood, and a list of the top n items can be produced. The effectiveness and accuracy of the proposed DRWMR system are tested on the MovieLens 100 K dataset and compared with the existing methods. Based on the evaluation results, the proposed DRWMR system gives precision (0.16), the root mean squared error (RMSE) of 0.73 and the recall (0.08), the mean absolute error (MAE) of 0.57, and the F − 1 measure (0.101).

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A Content-Based Recommendation System Using Neuro-Fuzzy Approach

Cited by 43 publications

References 14 publications

Mobility-aware personalized service recommendation in mobile edge computing

Mobility-aware personalized service recommendation in mobile edge computing

Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

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