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Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.
Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to
Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF.
This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well.
Conclusion: The motive of the work is to identify the best technique for recommending locations with the
highest accuracy and allow users to choose from a plethora of available locations; the best and interesting
location based on the individual’s profile.
Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models.
Limitations: User’s contextual information like demographics, social and geographical preferences have
not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.