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Privacy towards GIS Based Intelligent Tourism Recommender System in Big Data Analytics
Published in Siddhartha Bhattacharyya, Václav Snášel, Indrajit Pan, Debashis De, Hybrid Computational Intelligence, 2019
Abhaya Kumar Sahoo, Chittaranjan Pradhan, Siddhartha Bhattacharyya
Tourism Recommender System (TRS) gives more focus on user preferences with free resources and tourist activities of a particular city. TRS requires some useful data which are explicitly collected by users through explicit and implicit feedback. This TRS system is designed and developed based on collaborative filtering techniques. There are mainly two types of recommender systems: user-based and item-based recommenders. Here item denotes tourist location. In the user-based recommender system, users give their choices and ratings on items. We can recommend that item to the user, which is not rated by that user with the help of user-based recommender engine, considering similarity among the users. In the item-based recommender system, we use similarity between items (not users) to make predictions from users. Data collection for a recommender system is the first job for prediction. Tourism-related data are generated from three primary sources such as users, devices and operations.
A new preference-based model to solve the cold start problem in a recommender system
Published in Xiaoling Jia, Feng Wu, Electromechanical Control Technology and Transportation, 2017
Kun Liu, Wei Liu, Xiaoyun Chen
With the rapid development of electronic commerce and social media, more and more users have joined platforms such as Amazon or Facebook. The huge amount of available information makes users overwhelmed and indecisive. Users have to spend much time and energy in searching for the information they wanted. Unfortunately, sometimes they cannot get the expected results. In order to tackle this situation, recommender systems have appeared to solve the overload information problem. Recommender systems aim to provide personalized service through analyzing the users’ behavior or products information, which makes it easier for them to get the products or information they wanted. Until now there are several widely used approaches in recommender systems.
The evolution of recommender systems: From the beginning to the Big Data era
Published in Matthias Dehmer, Frank Emmert-Streib, Frontiers in Data Science, 2017
Beatrice Paoli, Monika Laner, Beat Tödtli, Jouri Semenov
Recommender systems help people to select the most suitable product from a huge amount of available options, based on their preferences, history of purchase, demographic information, and so on. The field of recommender systems emerged as an independent research area in the mid-1990s with first papers on collaborative filtering [1–3] opening new opportunities to retrieve personalized information on the Internet. There are different ways to reach the goal of recommending items to people; therefore, researchers developed many recommender systems for almost every domain such as entertainment, social networking, e-commerce, tourism, and so on.
Personalised context-aware re-ranking in recommender system
Published in Connection Science, 2022
Xiangyong Liu, Guojun Wang, Md Zakirul Alam Bhuiyan
As a new network model, the Internet of Things (IoT) can be used as integrating physical objects, the Internet, and semantics. In IoT, various types of devices are used to perceive and collect a large amount of information in the physical world, such as sound, temperature, heat, location, etc., through available networks. This information provides a solid foundation for people to enjoy various services through the processing of information technology. As an essential information filtering tool, the recommender system can help establish the association between objects and services, and it has become a key technology in various solutions of the IoT. We have entered the era of recommendation. Nowadays, recommender systems are widely used in existing commercial systems, such as Taobao, YouTube, Amazon, etc.
Security analysis of smart contract based rating and review systems: the perilous state of blockchain-based recommendation practices
Published in Connection Science, 2022
Jitendra Singh Yadav, Narendra Singh Yadav, Akhilesh Kumar Sharma
Nowadays, the digitalisation of commercial and non-commercial activities with the ease of internet reachability has resulted in the unprecedented growth of online information (Zhou et al., 2021). Users of the system can use this data in decision-making (e.g. which movie should I rent? Which book should I purchase? Which food should I order?). It is cumbersome for the users to investigate the vast amount of data. Recommender systems ease consumers’ tasks by suggesting more relevant items using the ratings and reviews provided by users, preferences, and personal information (Beloglazov et al., 2012). The well-known commercial systems, e.g. YouTube, Amazon, etc., widely use recommender systems (Liu et al., 2021). On the other hand, service providers may get increased revenue per their ranking and cross-product references. Most of these systems focus on accuracy of recommendations. The current types of threats e.g. modifying user ratings, promoting paid products in cross reference have drawn the attention of researchers.
CF-AMVRGO: Collaborative Filtering based Adaptive Moment Variance Reduction Gradient Optimizer for Movie Recommendations
Published in International Journal of Computers and Applications, 2022
V. Lakshmi Chetana, Hari Seetha
In recent decades, the recommender system has become an emerging research area that learns user’s preferences to develop effective recommendations [1]. The recommender system is highly used in several areas of interest such as movie recommendation, music recommendation, e-commerce, electronic book recommendation, intelligent marketing, and the tourism industry [2]. The main aim of the recommendation system is to automatically suggest the items such as web pages, music, products, news, movie, etc. for the users based on their historical preferences [3]. Movie recommendation is the most extensively utilized application in combination with an online multimedia platform that assists customers in accessing the preferred movies from a large movie library [4,5]. Usually, recommender systems are classified into four types known as collaborative filtering recommendation system, content-based recommendation system, knowledge-based recommendation system and hybrid recommendation system. Among the prior recommendation systems, collaborative filtering recommendation is effective, where it predicts the user rating based on similar user’s preference [6]. The majority of the developed collaborative filtering recommendation algorithms operate by identifying similar users, and then predict the movie rating based on user preferences, and their previous ratings. However, the existing collaborative filtering algorithms are affected by scalability, cold start issue, and data sparsity [7]. Hence, the recommendation accuracy is reduced if the user-item interaction matrix is sparse [8].