Restaurant Recommendation System Based on TF-IDF Vectorization: Integrating Content-Based and Collaborative Filtering Approaches

DOI

10.2991/978-94-6463-370-2_62How to use a DOI?

Keywords

Collaborative filtering; Content-based filtering; Cosine similarity-IDF Vectorization; Dine Rank algorithm; Deep Learning Enhanced TF-Digraph-based Recommendation

Abstract

In the contemporary data-centric era, recommendation systems play an essential role in enhancing the digital user experience by converting the vast expanse of information into tailored streams aligned with individual preferences. This research delves deep into the nuanced mechanisms anchoring these systems, shedding light on recent advancements in TF-IDF (term frequency–inverse document frequency) Vectorization, collaborative filtering, and the integration of deep learning. Through the implementation of techniques such as neural collaborative filtering, attention-driven models, and graph-centric neural networks, the efficacy of these methodologies in enhancing user-item interactions is critically examined. The results underscore that today’s recommendation algorithms, augmented with deep learning and sophisticated vector representations, effectuate a marked evolution in the precision and contextual relevance of suggestions. Such advancements not only set the stage for a more individualized digital interface but also highlight the potential of merging time-tested recommendation strategies with innovative deep learning approaches.

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.

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TY  - CONF
AU  - Sen Zhang
PY  - 2024
DA  - 2024/02/14
TI  - Restaurant Recommendation System Based on TF-IDF Vectorization: Integrating Content-Based and Collaborative Filtering Approaches
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 610
EP  - 618
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_62
DO  - 10.2991/978-94-6463-370-2_62
ID  - Zhang2024
ER  -