Content and Location Based Point-of-Interest Recommendation System Using HITS Algorithm

Abstract

A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users’ options and actions by bridging the gap between the physical and digital worlds. This expansion of wide geo-spatial datasets has inspired studies into novel recommender systems that aim to make users’ travels and social interactions easier. This huge amount of data generated by these devices has prompted an increase in the number of research activities and procedures aimed at breaking down and recovering useful data based on these large datasets. The aim of this challenge is to study GPS directions from a variety of people, as well as examine and apply computational strategies to recover useful data from those directions, useful data from GPS directions, including areas of interest and people’s proximity, and then create an instrument for information representation. This paper demonstrates how data mining techniques is used to recover valuable information from spatial data. As well as how such data can be useful in understanding people and areas within a district. Depending on the outcomes, we recommend a HITS (Hypertext Induced Topic Search) based POI recommendation calculation that can take into account the effect of social connections when recommending POIs to individual users.