Temple Recommendation Engine for Route Planning Based on TPS Clustering CNN Method

Abstract

There are no restrictions on religious or cultural practices in India. India’s temples are becoming an ideal platform for Hindu groups to express their ideals in a global context. For the sake of devotees, temples now require widespread accessibility and participation by a wide range of individuals on major holidays. A pilgrim may be unable to determine which site to visit, or where to stay, due to a variety of considerations such as cost, location, and the interests of each individual user. A user’s preferences are taken into consideration when a personalized recommendation list is generated. A large number of systems use Collaborative Filtering to produce user recommendations. In order to generate user-specific recommendations, this system uses a filtering method dubbed the “hybrid approach”. The Proposed OTPS Cluster technique is used to determine TPS (Time, Place, and Service). Users’ interests and TPA recommendations are taken into account. Users can forecast the location of the temple based on the temple’s history. Collaborative Filtering and Material-Based Filtering were used to propose sites based on comparable users and content, respectively. Testing shows that the algorithm is capable of solving difficulties in standard tour routing and providing a temple visit route that is tailored to each individual’s preferences. This study makes use of data from the South Indian city of Temple in the form of temples.