Oniomania Similarity Search with Recommended System
K.Karpaga Priyaa1, C.Saranya2, PA.Yogaalakshmi3, M.Haripriya4
1K.Karpaga Priyaa, Assistant professor, CSE department, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.
2C.Saranya, Assistant professor, CSE department, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.
3PA.Yogaalakshmi, Student, Engineering Degree in CSE, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.
4M.Haripriya, Student, Engineering Degree in CSE, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.
Manuscript received on 11 August 2019. | Revised Manuscript received on 4702-4705 August 2019. | Manuscript published on 30 September 2019. | PP: 3604-3608 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6851098319/2019©BEIESP | DOI: 10.35940/ijrte.C6851.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Users shopping online prefer to quickly access products that they are in need of among a list of similar products. The similarity metrics in use, only contemplates on the product attributes which doesn’t always meet the user expectations. The current system relies on solving this by performing Θ-similarity and r-nearest neighbor, where product similarity is considered only if it satisfies a user-preference list, by hitting the reverse top-k queries results. However, the products expressed here are generally based on the user’s preference, which can break the user independence of the product. To conquer these drawbacks, we use a more advanced system which represents feature based products such as Usercentric Model, where we have two phases. At first, we gather opinion based data about the feature, and subsequently these feature similarities are ranked. The ranking is predicted by collaborative filtering recommendation algorithm. Next, the rank generated from the above algorithm will be compared with existing data to get the top-k best product by performing No-Random Access algorithm (NRA) in second phase. Through this users are granted the top.
Index Terms: Opinion Mining, Collaborative Filtering Algorithm, NRA Algorithm.
Scope of the Article: Search-Based Software Engineering