Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

<Previous Article In Volume
Next Article In Volume>

Collaborative Filtering and Sentiment Analysis: Basics to Build a Map Recommender System

Authors
Zimu Guan1, *
1Department of Computer Science, University of California, San Diego, La Jolla, CA, 92093, USA
*Corresponding author. Email: ziguan@ucsd.edu
Corresponding Author
Zimu Guan
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_34How to use a DOI?
Keywords
Recommender System; Collaborative Filtering; Sentiment Analysis
Abstract

Recommendation systems are gaining increasing popularity in today’s society, aiming to cater to users’ potential preferences for the convenience of users and as part of businesses’ commercial strategies. Just as product recommendations have proven effective, location recommendations are also becoming enticing. In our increasingly stable daily lives, more individuals are inclined to receive suggestions for new places that align with their preferences. Even with the popularity of Google Map, recommender system on locations should still be very useful in practice with applications. This paper summarizes some basic models to understand for building recommender systems for maps. Collaborative filtering, sentiment analysis, and feature key-word clustering are all very crucial in constructing the model, with the requirement of user’s review, ratings, and location data. Several successful systems to possibly integrate within the system includes categorization labels on users, MERLOT and VADER system, and GeoSCAN system for considering location in recommendations. From researchers in different studies, it is proven possible to mix the analysis of ratings and reviews together to form a better algorithm for recommendation. The result of combination of these systems can overcome each other’s shortcomings.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_34
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_34How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zimu Guan
PY  - 2024
DA  - 2024/02/14
TI  - Collaborative Filtering and Sentiment Analysis: Basics to Build a Map Recommender System
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 313
EP  - 320
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_34
DO  - 10.2991/978-94-6463-370-2_34
ID  - Guan2024
ER  -