Personality Aware Product Recommendation System [Reccokart]

Registration ID: IJNRD_185151

Published ID: IJNRD2212242

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Patil Umesh Bhagvan, Bobade Ganesh Dattatray, Khomane Rohit Rajendra, Mane Raviraj Babaso, Nale Rajesh Keshav

social networks; social computing; user interest mining; user modeling personality computing; product recommendation; recommendation system.

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.

Published Paper   E-Certificate

Patil Umesh Bhagvan, Bobade Ganesh Dattatray, Khomane Rohit Rajendra, Mane Raviraj Babaso, & Nale Rajesh Keshav (December-2022). Personality Aware Product Recommendation System [Reccokart]. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(12), c345-c353. https://ijnrd.org/papers/IJNRD2212242.pdf

Volume 7 Issue 12, December-2022

Pages : c345-c353

Paper Reg. ID: IJNRD_185151

Published Paper Id: IJNRD2212242

Downloads: 000122021

Research Area: Engineering

Author Type: Indian Author

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2212242.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2212242

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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