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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: | BOOK RECOMANDATION SYSTEM USING MACHINE LEARNING |
| Authors Name: | PROF. VAISHALI V. JIKAR , MR. HRUSHIKESH THORAT , MR. VRUSHABH BARASKAR , Ms. JANVI PANDEY , Ms. RESHMA ZADE |
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IJNRD_211946
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| Published Paper Id: | IJNRD2401227 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Recommendation System (RS) is software that suggests similar items to a purchaser based on his/her earlier purchases or preferences. RS examines huge data of objects and compiles a list of those objects which would fulfil the requirements of the buyer. Nowadays most ecommerce companies are using Recommendation systems to lure buyers to purchase more by offering items that the buyer is likely to prefer. Book Recommendation System is being used by Amazon, Barnes and Noble, Flipkart, Goodreads, etc. to recommend books the customer would be tempted to buy as they are matched with his/her choices. The challenges they face are to filter, set a priority and give recommendations which are accurate. RS systems use Collaborative Filtering (CF) to generate lists of items similar to the buyer’s preferences. Collaborative filtering is based on the assumption that if a user has rated two books then to a user who has read one of these books, the other book can be recommended (Collaboration). CF has difficulties in giving accurate recommendations due to problems of scalability, sparsity and cold start. Therefore this paper proposes a recommendation that uses Collaborative filtering with Jaccard Similarity (JS) to give more accurate recommendations. JS is based on an index calculated for a pair of books. It is a ratio of common users (users who have rated both books) divided by the sum of users who have rated the two books individually. Larger the number of common users higher will be the JS Index and hence better recommendations. Books with high JS index (more recommended) will appear on top of the recommended books list. |
| Keywords: | Similarity index, filtering techniques, recommender system, Jaccard Similarity. |
| Cite Article: | "BOOK RECOMANDATION SYSTEM USING MACHINE LEARNING ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.c207-c215, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401227.pdf |
| Downloads: | 000118751 |
| 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:IJNRD2401227 Registration ID: 211946 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: c207-c215 Country: WARDHA, MAHARASHTRA , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401227 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401227 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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