Group Recommendation for Cold Start Users Using Hybrid Recommendation Technique 
Harleen Kaur1, Gourav Bathla2
1Harleen Kaur, Chandigarh University, Gahruan, Mohali (Punjab), India.
2Gourav Bathla, Chandigarh University, Gahruan, Mohali (Punjab), India.
Manuscript received on 27 August 2019 | Revised Manuscript received on 03 September 2019 | Manuscript Published on 14 September 2019 | PP: 452-456 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10950785S319/19©BEIESP | DOI: 10.35940/ijeat.E1095.0785S319
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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: Recommender system is an data retrieval system that gives customers the recommendations for the items that a customer may be willing to have. It helps in making the search easy by sorting the huge amount of data. We have progressed from the era of paucity to the new era of plethora due to which there is lot of development in the recommender system. In today’s scenario the interaction between the groups of friends, family or colleagues has increased due to the advancement in mobile devices and the social media. So, group recommendation has become a necessity in all kinds of domains. In this paper a system has been proposed using the group recommendation system based on hybrid based filtering method to overcome the cold start user issue which arises when a new user signs in and he/she doesn’t have any past records. So, the recommender system does not have enough information related to the user to recommend an item which will be of his/her interest. The dataset has been taken from the MovieLens is used in the experiment.
Keywords: Cold Start Problem, Group Recommendation System, Hybrid Filtering Approach.
Scope of the Article: System Integration