JETIREXPLORE- Search Thousands of research papers



Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006393

Page Number

366-368

Share This Article


Title

Survey on Scalable Content-Aware Collaborative Filtering for location recommendation

ISSN

2349-5162

Cite This Article

"Survey on Scalable Content-Aware Collaborative Filtering for location recommendation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.366-368, June-2020, Available :http://www.jetir.org/papers/JETIR2006393.pdf

Abstract

The Location recommendation plays an essential role in helping people find interesting places. Although recent research has he has studied how to advise places with social and geographical information, some of which have dealt with the problem of starting the new cold users. Because mobility records are often shared on social networks, semantic information can be used to address this challenge. There the typical method is to place them in collaborative content-based filters based on explicit comments, but require a negative design samples for a better learning performance, since the negative user preference is not observable in human mobility. However, previous studies have demonstrated empirically that sampling-based methods do not work well. To this end, we propose a system based on implicit scalable comments Content-based collaborative filtering framework (ICCF) to incorporate semantic content and avoid negative sampling. We then develop an efficient optimization algorithm, scaling in a linear fashion with the dimensions of the data and the dimensions of the features, and in a quadratic way with the dimension of latent space. We also establish its relationship with the factorization of the plate matrix plating. Finally, we evaluated ICCF with a large-scale LBSN data set in which users have text and content profiles. The results show that ICCF surpasses many competitors’ baselines and that user information is not only effective for improving recommendations, but also for managing cold boot scenarios.

Key Words

Content-aware, implicit feedback, Location recommendation, social network, weighted matrix factorization.

Cite This Article

"Survey on Scalable Content-Aware Collaborative Filtering for location recommendation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp366-368, June-2020, Available at : http://www.jetir.org/papers/JETIR2006393.pdf

Publication Details

Published Paper ID: JETIR2006393
Registration ID: 234589
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 366-368
ISSN Number: 2349-5162

Download Paper

Preview Article

Download Paper




Cite This Article

"Survey on Scalable Content-Aware Collaborative Filtering for location recommendation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp366-368, June-2020, Available at : http://www.jetir.org/papers/JETIR2006393.pdf




Preview This Article


Downlaod

Click here for Article Preview