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Improved Hybrid deep collaborative filtering Approach for True Recommendations

Computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid deep collaborative filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,deep collaborative filtering,Neural Sentiment Classifier,and deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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