Deep Learning-based Mobile Tourism Recommender System

International Journal of Advanced Computer Science and Applications

Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

Artificial Intelligence Evolution

In today’s times, the recommended system is a very powerful weapon of shoppers that is very helpful in advancing the Internet, personalized tendencies, and online shopping. The recommended system is used primarily for commercial benefit. The recommended system works on the strength of the user’s past shopping experience and its feedback, whether it is positive or negative. Hence the recommended system is also an innovative method. There is a deferred method of the recommended system which has its own advantages and disadvantages. In this paper, the Recommender system based on deep learning is proposed, and also discussed the challenges and issues which are related to the deep learning based recommender system. i.e., Accuracy, Cold Start Problem, Scalability States etc. In this paper, we have also discussed the work done so far, which has been given by various scientists / researchers / investigators. Advancement of machine learning and deep learning is very big, in today’s era. This...

Revue d'Intelligence Artificielle

With the development of machine learning, to improve the accuracy in recommendation systems, the main purpose of the suggested approach consists of using techniques and algorithms which can predict and suggest relevant tourist services (k-items) to users according to their interests, needs, or tastes. In this research, we describe how machine learning techniques can automatically provide personalized recommendations to the requestors by considering both their preferences and their implicit/explicit contextual information and the current contextual constraints of the points of interest. We build an efficient, intelligent H-RN algorithm that hybridizes both the most known machine learning algorithms, namely Random Forest and Naïve Bayes, with both collaborative filtering techniques (model-based and memory-based technique). Different experiments of our approach as part of a recommender system in the touristic field are performed over the four large real-world datasets. Recommender syst...

Advances in Modelling and Analysis B, 2018

Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is challenging task to e-commerce companies. Traditional Recommender Systems provided the solutions in recommending the products. This in turn help companies to generate good revenue. Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms. These algorithms can be used to give recommendations to users to purchase products. In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared.

Many companies have realized the importance of deep learning, which justifies their growing interest in the use of recommender systems to boost their sales. They aim to predict users' intents and recommend products likely to be of their interests. The purpose of this study is to provide a review of deep learning techniques for recommendation systems that have been used in research and industry.

ISPRS International Journal of Geo-Information

With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology have a limitation in that they cannot properly classify unique photos appearing in specific tourist attractions with predetermined photo categories such as Places365 or ImageNet dataset or it takes a lot of time and effort to build a separate training dataset to train the model and to generate a tourism photo classification category according to a specific tourist destination. The purpose of thi...

Personal and Ubiquitous Computing, 2019

In a ubiquitous computing scenario, characterized by pervasive technologies, tourists can get assistance from mobile technologies in planning their trips. In a context where more and more people own smartphones, tourists expect to get personalized suggestions just in time whenever and wherever they need. To be effective, mobile applications for travel recommendation should consider both the variability of the user's interests and an effective way to express them while interacting with the environment. This paper presents LOOKER, a mobile recommender system for tourism and travelrelated services that considers the above-described issues. It is an adaptable application developed for the Android platform, which takes into account basic contextual information such as location and time, and implements a content-based filtering (CBF) strategy to make personalized suggestions based on the user's tourism-related user-generated content (UGC) s/he diffuses on social media. Specifically, the CBF strategy implemented in LOOKER is based on a multi-layer user profile, where the layers representing distinct travel-related service categories (e.g., restaurants, hotels, points of interest) are modeled via language models that are defined on the basis of the captured UGC. This allows inferring the interests and the opinions of travelers about the available items. To evaluate the usefulness and the usability of the LOOKER mobile application, user studies have been conducted. The positive outcomes that have been obtained illustrate the potentials of LOOKER.

2018

On the Internet, where the number of choices is overwhelming, there is a need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem for many Internet users. Recommender systems solve this problem by searching through large volumes of dynamically generated information to provide users with personalized content and services. In view of more personalized clothing requirements, an intelligent clothing recommendation system was designed and developed in this paper. By the use of Transfer Learning to elicit the rich information from the product images, and the use of cosine similarity approach, the user is provided with eclectic recommended products depending on their choices. This was implemented on a web e-commerce application, where users have a wide range to choose their clothing apparel from our database. Only those images are recommended to users which have 80% or above similarity with the product chosen by the user, which helps in solving personalized recommendations.

ISPRS International Journal of Geo-Information, 2020

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.