Recommender system for Persian blogs

Borhanifard, Zeinab; Minaei-Bidgoli, Behrouz

doi:10.1109/icwr.2017.7959314

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The authors collaborate many existing algorithms which fetch and filter the blog posts based on the interests of users and recommend whenever the user logs in. In the paper, a model was discussed which adopts the clustering algorithms and the personalized search results for Persian blogs [17]. Zulkefli and Bin Baharudin discussed the importance of recommendations in the field of hotels.…”

Section: Related Workmentioning

confidence: 99%

Adaptive and Personalized Web Blog Searching Technique Using S-ANFIS

et al. 2022

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Day by day, the number of blog users and microblog users is increasing worldwide. It is easy to say that blogs have captured a significant portion of other web services. In the past few years, the number of users has exponentially increased. User count of Facebook, Twitter, and Instagram applications is not hidden from anyone. Users on such platforms share ideas, experiences, stories, opinions, and views and want to interact with people with the same set of interests. As per the user’s expectation, there is a requirement of two things: content curation and recommendations. The content curation algorithm will find the people and their posts on personalized search results. In addition, the recommendation system will help to find the most appropriate match to interact with. In this paper, both approaches are combined to show the user’s curated and recommended results. The article focuses on the hybrid model named S-ANFIS, and the results are compared with the well-known approaches like ANN, Deep Neural Network (DNN), and Recurrent Neural Network (RNN).

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Section: Related Workmentioning

confidence: 99%