Implementation of Product Recommendation System Based on User Interest, Location and Social Circle

Authors(2) :-Punam Suryawanshi, Prof. Pragati Patil

Recommendation System (RS) is used to find users interested things. With the start of social system, people are interested to share their experience, for example, rating, reviews, etc that has any sort of impact to recommend the things of user interest. Scarcely any recommendation systems has suggested that rely upon collaborative filtering, content based filtering and hybrid recommendation approachs. The present recommendation system isn't productive as need. It needs to require improvement in structure for present and future necessities to getting best results for recommendation qualities. This paper uses four variables, for instance, social segments, personal interest similarity, interpersonal impact and user's location information. Mix of these segments is utilized into a united personalized recommendation show which is depends upon probabilistic system factorization. In propose system we incorporate user location in dataset moreover use PCC comparability procedure which reduce bumbles and connection rules mining using FP-Growth which improves the exactness.

Authors and Affiliations

Punam Suryawanshi
PG Scholar, Department of Computer Science Engineering, Abha-Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India.
Prof. Pragati Patil
Assistant Professor, Department of Computer Science Engineering, Abha-Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India.

Interpersonal Influence, Personal Interest, Recommender System, Social Networks.

  1. X. -W. Yang, H. Steck, and Y. Liu. Circle-based recommendation in online social networks.KDD-12, pp. 1267-1275, Aug.2012.
  2. M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. -W. Zhu and S. -Q. Yang. Social contextual recommendation. CIKM-12, pp. 45-54,2012.
  3. M. Jamali and M. Ester.A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. ACM conference on Recommender systems (RecSys), 2010.
  4. R. Salakhutdinov and A. Mnih.Probabilistic matrix factorization. In NIPS 2008, 2008.
  5. M.E. Tipping and C.M. Bishop. Probabilistic principal component analysis.Journal of the Royal Statistical Society, Series B, pp. 611-622, 1999.
  6. G. Adomavicius, and A. Tuzhilin. Toward the next generation of recommender systems: a survey of the state of- the-art and possible extensions.Knowledge and Data Engineering, IEEE Transactions on, pp. 734-749, Jun. 2005.
  7. R. Bell, Y. Koren, and C. Volinsky.Modeling relationships at multiple scalesto improve accuracy of large recommender systems.KDD-7, pp. 95-104,2007.
  8. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl.Item-based collaborative filtering recommendation algorithms.In WWW, pp. 285-295, 2001.
  9. M. Jahrer, A. Toscher, and R. Legenstein.Combining predictions for accurate recommender systems.KDD10, pp. 693- 702, 2010.
  10. Y. Zhang, B. Cao, and D.Y. Yeung.Multi-domain collaborative fltering. InProc.UAI, 2010.

Publication Details

Published in : Volume 6 | Issue 3 | May-June 2019
Date of Publication : 2019-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 59-65
Manuscript Number : IJSRST196310
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Punam Suryawanshi, Prof. Pragati Patil, " Implementation of Product Recommendation System Based on User Interest, Location and Social Circle", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 6, Issue 3, pp.59-65, May-June-2019.
Journal URL : https://ijsrst.com/IJSRST196310
Citation Detection and Elimination     |      | | BibTeX | RIS | CSV

Article Preview