Correction to: Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

Rodpysh, Keyvan Vahidy; Mirabedini, Seyed Javad; Banirostam, Touraj

doi:10.1007/s10660-021-09497-6

Cited by 2 publications

(1 citation statement)

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“…Second, as the total number of products and users increases, the number of products viewed by users will only decrease. The proportion of evaluation items to the total is even lower [9]. The sparsity problem can cause inefficiency, low precision, and low adequacy for similarity computation.…”

Section: Introductionmentioning

confidence: 99%

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Yun¹,

Jiao

2022

Wireless Communications and Mobile Computing

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As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.

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Section: Introductionmentioning

confidence: 99%