Location Based Recommender Systems: Architecture, Trends and Research Areas

Tiwari, Sunita; Kaushik, Saroj; Tiwari, Shivendra; Jagwani, Priti

doi:10.1049/cp.2012.2096

Cited by 11 publications

(5 citation statements)

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“…It also gives a more appropriate choices for the user from the large dataset. [13] People can find what they want by using the recommendation system, which suggests potential products of interest to them. It frequently uses existing associations between users and/or things to predict people's preferences for certain items.…”

Section: Literature Surveymentioning

confidence: 99%

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

et al. 2023

Int. J. Res. Publ. Rev.

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In the recent years, there has been a rise in immigration. Most of these immigrants are students and employees. Most of these individuals require long-term, affordable accommodation based on their preference. When these individuals arrive at their destination, one of the major problems faced by them is accommodation.Due the language and cultural barrier they find it difficult to search a suitable Hostel or PG. This project involves identifying accommodation for these students/Employees according to their choices for amenities, affordability and proximity to the place in order to apply K-Means clustering to find the best accommodations in the chosen location. Recommendation system helps to estimate and predict user preference, our project uses content-based filtering as the recommendations are specific to user.

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Section: Literature Surveymentioning

confidence: 99%

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

et al. 2023

Int. J. Res. Publ. Rev.

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“…RS domain has been well explored in the past couple of decades (Tiwari, March 2015;Tiwari, 2012;Adomavicius, 2011;Berjani, 2011;Adomavicius, 2005;Burke 2002). A lot of contributions can be found in the field of news recommendation systems, and some work can be found in the tweet recommendation system as well.…”

Section: Related Workmentioning

confidence: 99%

PNTRS

Tiwari

Kumar

Jethwani

et al. 2021

Journal of Cases on Information Technology

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A news recommendation system not only must recommend the latest, trending and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level and it is 7.9 on the scale of 10.

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“…Recommender Systems is straight fitted into Location Based Services (LBS) field. Providing suitable and adapted recommendation location is a salient exploration trick [1].…”

Section: Location Based Recommendation Systemmentioning

confidence: 99%

Location Based Recommender Systems (LBRS) – A Review

Surendiran

2020

IFIP Advances in Information and Communication Technology

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Recommender system has a vital role in everyday life with newer advancement. Location based recommender system is the current trend involved in mobile devices by providing the user with their timely needs in an effective and efficient manner. The services provided by the location based recommender system are Geo-tagged data based services containing the Global Positioning System and sensors incorporated to accumulate user information. Bayesian network model is widely used in geo-tagged based services to provide solution to the cold start problem. Point Location based services considers user check-in and auxiliary information to provide recommendation. Regional based recommendation can be considered for improving accuracy in this Point location based service. Trajectory based services uses the travel paths of the user and finds place of interest along with the similar user behaviours. Context based information can be incorporated with these services to provide better recommendation. Thus this article provides an overview of the Geo-tagged media based services and Point Location based services and discusses about the possible research issues and future work that can be implemented.

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Location Based Recommender Systems: Architecture, Trends and Research Areas

Cited by 11 publications

References 0 publications

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

PNTRS

Location Based Recommender Systems (LBRS) – A Review

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