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A Deep Neural Networks model for Restaurant Recommendation systems in Thailand

Published: 21 June 2022 Publication History

Abstract

In the age of flooded information, Recommender Systems play a crucial role as long as consumers consume more content and submit more data. Many businesses have implemented Recommender Systems to assist users find items based on their previous interactions. Deep neural networks have demonstrated promising results in a variety of disciplines, including recommendation systems in the past few years. However, such studies ignore auxiliary information input. In this work, we purpose a deep recommendation system with neural networks which consists of deep collaborative filtering to learn user and item interaction latent factor and enrich the performance with textual information by using multi-layer perceptrons and combining these two models under our framework, called DNNRecs. Apart from our model framework, we also contribute a feature engineering method to create new features from review text by using technique tf-idf. Extensive experiments on one real-life dataset in Thailand demonstrate the effectiveness of the proposed model.

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ICMLC '22: Proceedings of the 2022 14th International Conference on Machine Learning and Computing
February 2022
570 pages
ISBN:9781450395700
DOI:10.1145/3529836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 June 2022

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Author Tags

  1. Auxiliary Information
  2. Information Retrieval
  3. Top-N recommender systems

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