Incorporating multidimensional information into dynamic recommendation process to cope with cold start and data sparsity problems

Kolahkaj, Maral; Harounabadi, Ali; Nikravanshalmani, Alireza; Chinipardaz, Rahim

doi:10.1007/s12652-020-02695-4

Cited by 12 publications

(4 citation statements)

References 65 publications

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“…e association rule discovery algorithm based on the aggregation tree has indeed achieved an improvement in efficiency. e interest characteristics of tourist routes are mainly summarized for tourists, because this study only cares about the correlation between tourist routes and users' interest characteristics [22]. Whether it is using aggregation tree to organize transactions, or improving the basic Apriori algorithm according to the characteristics of data to be mined and the characteristics of rule formation, it is successful.…”

Section: Personalized Tourism Recommendation Algorithmmentioning

confidence: 99%

Tourism Destination Recommendation Based on Association Rule Algorithm

Lou

2022

Mobile Information Systems

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With the development of information technology and the arrival of intelligent era, the tourism industry has also changed from traditional information service to intelligence. In this study, the recommendation system is studied. By comparing the advantages and disadvantages of various recommendation methods, the association rule algorithm is selected as the recommendation method in this study. Then, the user’s operation behavior is obtained and the personalized tourism destination recommendation model is established. This model is based on the user’s access operation behavior data, and it introduces the user’s interest degree as a parameter to measure the operation behavior data, forming the basic data of data mining. The popularity of tourist routes and the change weight of longitudinal interest characteristics among users are introduced into the algorithm. The change weight of user’s lateral interest characteristics is added to the prediction of target users’ future interest. Compared with the classified recommendation method, users can easily locate the information of scenic spots they are interested in under the guidance of scenic spot recommendation. The results further verify that the research of this model has certain practical significance and practical value.

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Section: Personalized Tourism Recommendation Algorithmmentioning

confidence: 99%

Tourism Destination Recommendation Based on Association Rule Algorithm

Lou

2022

Mobile Information Systems

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“…Besides, to ensure the accuracy of the recommendation items, the current Recommender System will take the similarity as the reference parameter of recommendation. In [23], the similarity between users is calculated to help get more accurate recommendation results. In [24], the similarity between users and items is calculated.…”

Section: Related Workmentioning

confidence: 99%

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

Wang

Huang

et al. 2022

Security and Communication Networks

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With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.

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“…Using methods based on fuzzy logic is one of the related solutions. Fuzzy logic is widely used in the design of a recommender system to manage uncertainty, inaccuracy, and ambiguity in item properties and user behavior [Kolahkaj et al,2021].There are usually many cases in digital businesses and each user evaluates only a part of the cases that lead to the challenge of sparse data through matrix factorization (MF) [ Vahidy Rodpysh et al,2021].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

Mirabedini

Rodpysh

Banirostam

2022

Preprint

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In recent years explosive increase and variety of information due to the progress of the internet. Therefore, decision making in different fields has faced different challenges. Recommender systems provide personalized services to users by identifying users' interests, filtering information, and managing information, especially contextual information. Similarity criteria based on contextual information is a way to reduce the challenge of starting a cold. We argue that similarity is a vague concept, and we more realistic results in recommender systems using fuzzy logic. Applying fuzzy logic to contextual information can be an effective way to identify ambiguities and uncertainties in measuring the similarity of items and users. In this paper, we present a new multilevel contextual fuzzy similarity criterion with SVD matrix for recommending systems called FCSVD Which is based on a combination of similarity criteria PSS, fuzzy rules and contextual information. The results show an improvement in the accuracy of the method recommendations FVSVD compared to the CACF, CTLSVD, MF-LOD and CFSVD methods according to evaluation measure Precision, Recall, F1-score.

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Incorporating multidimensional information into dynamic recommendation process to cope with cold start and data sparsity problems

Cited by 12 publications

References 65 publications

Tourism Destination Recommendation Based on Association Rule Algorithm

Tourism Destination Recommendation Based on Association Rule Algorithm

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

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