A Collaborative Location-Based Personalized Recommender System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

With the rapid development of information and communication technology, numbers of tourists are increasing all over the world due to the easy way to plan for the tour. Location-based recommender system considers both user’s behavior and preference for recommendation process. In this paper, we have proposed a location-based personalized recommender system which offers a set of spots to the tourist by considering the place, food, and product preference of the tourists. The proposed system uses collaborative filtering technique to recommend the best spots along with food availability and product availability to the tourist according to the opinions of the local users who already visited those spots. Cosine similarity measure is used to find the local users who are similar to the given query user. The results revealed that collaborative filtering is the more reliable technique for personalized recommender systems. The proposed system is evaluated in terms of precision, recall, and f-measure values.

Keywords

Recommender systems Collaborative filtering Location-based Cosine similarity 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUtkal UniversityBhubaneswarIndia
  2. 2.Deaprtment of Computer Science & Engg.Gandhi Institute for TechnologyBhubaneswarIndia

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