Asymmetrically Weighted Cosine Similarity Measure for Recommendation Systems

Mishra, Sahil; Panda, Sanjaya Kumar

doi:10.1007/978-981-19-1018-0_42

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…However, these metrics regard any resemblance between two users or products as equivalent. According to Mishra et al [15], the AWCS enables the same features in many vectors with varying weights. This implies that a given user may value "action" differently from another, and the similarity calculation takes these different feature weights into effect.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

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Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.

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Section: Literature Reviewmentioning

confidence: 99%

“…The similarity between two users who have evaluated the same QoS features can be determined using weighted cosine similarity. To compute the similarities between users, we choose the algorithm of AWCS [15]. According to Filali and Yagoubi [49], their approach is beneficial for determining how similar two users are.…”

Section: Rating Similaritiesmentioning

confidence: 99%

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

View full text Add to dashboard Cite

show abstract

Asymmetrically Weighted Cosine Similarity Measure for Recommendation Systems

Cited by 1 publication

References 13 publications

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

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