Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations Article Swipe
Muhammad Ibrahim
,
Imran Sarwar Bajwa
,
Nadeem Sarwar
,
Haroon Abdul Waheed
,
Muhammad Zulkifl Hasan
,
Muhammad Zunnurain Hussain
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.032856
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.032856
Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To 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 not. The 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 problems. Experimental 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.
Related Topics
Concepts
Collaborative filtering
Computer science
Deep learning
Artificial intelligence
Classifier (UML)
Recommender system
Cold start (automotive)
Product (mathematics)
Machine learning
Sentiment analysis
Confusion
Multivariate statistics
Information retrieval
Data mining
Data science
Engineering
Mathematics
Aerospace engineering
Psychology
Psychoanalysis
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.032856
- https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_32856/TSP_CMC_32856.pdf
- OA Status
- diamond
- Cited By
- 15
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313328030
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313328030Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/cmc.2023.032856Digital Object Identifier
- Title
-
Improved Hybrid Deep Collaborative Filtering Approach for True RecommendationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
-
Muhammad Ibrahim, Imran Sarwar Bajwa, Nadeem Sarwar, Haroon Abdul Waheed, Muhammad Zulkifl Hasan, Muhammad Zunnurain HussainList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2023.032856Publisher landing page
- PDF URL
-
https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_32856/TSP_CMC_32856.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_32856/TSP_CMC_32856.pdfDirect OA link when available
- Concepts
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Collaborative filtering, Computer science, Deep learning, Artificial intelligence, Classifier (UML), Recommender system, Cold start (automotive), Product (mathematics), Machine learning, Sentiment analysis, Confusion, Multivariate statistics, Information retrieval, Data mining, Data science, Engineering, Mathematics, Aerospace engineering, Psychology, Psychoanalysis, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 6, 2023: 6Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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