An Efficient Personalized POI Recommendation using PCA-SVM based Filtering and Classification

Abstract

The rapid growth of cities has developed an increasing number of points of interest (POIs), e.g., restaurants, stores, hotels, etc; to enrich people's life, providing us with more choices of life experiences than before. People are willing to explore the city and neighborhood in their daily life and decide "where they should go" according to their personal interest and various choices of POIs. The Existing Methodology implemented for the Filtering of POI Recommendation is efficient but contains less Precision and Recall, hence a new and efficient technique for the POI Recommendation using Principle Component Analysis with Support Vector Machine Learning is proposed which provides more efficient results in comparison.

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

“…In the model, the monitoring data is stored in a unified database. By "learning" the relationship probability between factors, such as nutrient load, nutrient concentration of lake water body and fishy concentration, it can be convenient for nonprofessional modelers to use, so as to significantly reduce water treatment costs and operating expenses [9]. Zhang et al put forward the concept of interest degree of attribute set through massive data and data flow analysis, studied the characteristic difference of attribute set as data, and deduced an accurate algorithm to find that the interest degree of all attribute sets exceeds the given threshold.…”