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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Personality Aware Product Recommendation System [Reccokart] |
| Authors Name: | Patil Umesh Bhagvan , Bobade Ganesh Dattatray , Khomane Rohit Rajendra , Mane Raviraj Babaso , Nale Rajesh Keshav |
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| Author Reg. ID: |
IJNRD_185151
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| Published Paper Id: | IJNRD2212242 |
| Published In: | Volume 7 Issue 12, December-2022 |
| DOI: | |
| Abstract: | Any modern social networking or online retail platform must have a recommendation system. A product recommendation is basically a filtering system that seeks to predict and show the items that a user would like to purchase. It may not be entirely accurate, but if it shows you what you like then it is doing its job right. As a typical illustration of a legacy recommendation system, the product recommendation system has two significant drawbacks: recommendation repetition and unpredictability about new items (cold start). Because the older recommendation algorithms only use the user's previous purchasing history when making recommendations, these limitations exist. The cold start and recommendation redundancy may be lessened by incorporating the user's social attributes, such as personality traits and areas of interest. In light of this, we present Meta-Interest, a personality- aware product recommendation system built on user interest mining and metapath discovery. The suggested method incorporates the user's personality qualities to forecast his or her themes of interest and to link the user's personality facets with the relevant things, making it personality-aware from two perspectives. The suggested system was evaluated against current recommendation techniques, including session- based and deep-learning-based systems. According to experimental findings, the suggested strategy can improve the recommendation system's memory and precision, particularly in cold-start conditions. |
| Keywords: | social networks; social computing; user interest mining; user modeling personality computing; product recommendation; recommendation system. |
| Cite Article: | "Personality Aware Product Recommendation System [Reccokart]", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 12, page no.c345-c353, December-2022, Available :http://www.ijnrd.org/papers/IJNRD2212242.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2212242 Registration ID: 185151 Published In: Volume 7 Issue 12, December-2022 DOI (Digital Object Identifier): Page No: c345-c353 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2212242 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2212242 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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