• DIPALI DOKE
• PRATIKSHA PAWAR
• POOJA GIRI
• SHITAL KAKAD

The Location recommendation plays an essential role in helping people find interesting places. Although recent researchers 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 reviews using machine learning, but require a positive design samples for a better learning performance, since the positive 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 using machine learning. We also establish its relationship with the factorization of the plate matrix plating. Finally, we evaluated ICCF with a large-scale hotel 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 start scenarios.

Implicit and Explicit feedback, Hotel recommendation, social network, collaborative filtering.

"Location Recommendation Using Content Aware Collaborative Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.416-421, May-2019, Available :http://www.jetir.org/papers/JETIR1905M66.pdf

"Location Recommendation Using Content Aware Collaborative Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp416-421, May-2019, Available at : http://www.jetir.org/papers/JETIR1905M66.pdf