An empirical investigation of the utilitarian, social benefits in LBS information disclosure—The moderating effect of the gender based social role theory

Due to the rapid growth of Internet, E-commerce and Internet of Things, people use Web based services for most of their needs including buying items, reading books, watching online shows etc. Several companies are using recommender systems to influence people’s choices based on their likings, behaviours etc. Hence, people fear that their privacy is violated. Also, some of the online applications are not safe and secure. One way to overcome the privacy related issues is using secured solutions such as incorporating blockchain technologies for privacy-based applications. The decentralized nature of blockchain technologies have resolved several security, and authentication problems of Internet of Things systems. In this paper, we conduct a comprehensive survey on the privacy solutions for recommender systems emphasising current status, classification and open issues. We also discuss blockchain technology, including its structure as well as applications of blockchain technology for privacy solutions of recommender systems. Furthermore, we discuss the limitations and delve into future trends that blockchain technology can be adapted for privacy-base applications in the years to come.

A Survey of Privacy Solutions using Blockchain for Recommender Systems: Current Status, Classification and Open Issues

Finding the information and making it available today is a very complex challenge. On the one hand, the large amount of available data requires a great ability to manage information; on the other hand, understanding the real needs of users requires complex systems that can provide contextual information. Italy's economy is based on tourism, thanks to its cultural heritage visited by millions of people every day. The goal of this paper is to create a recommender system to furnish a tailor-made story for the user based on the context in which it is located, thanks to a smart app that can provide contextual information. Using a Chatbot, this system also provides a context awareness data in order to enhance the tourist experience.

A Context Aware Recommender System for Digital Storytelling

Recommender systems play the role of leading users to customized suggestions in the broad universe of available possibilities. While producers use it for cross-selling, which suggests additional products or services to customers, consumers use recommender systems to seek items that match their interests and preferences. By establishing a value-added relationship between the system and the customer, recommender systems boost loyalty. In present e-commerce systems, user pattern search, item, and historical analysis is a substantial component of a recommendation system. A better recommendation system based on product specifications and product similarity measures rather than historical data could lead to a progressive change in e-commerce recommendation technologies. This paper proposes a model that uses product specifications and various similarity measures to compute the user recommendations. The model considers product description and specifications to calculate a similarity measure and then uses these similarity values to form clusters of products. Based on the generated cluster of products, relevant products are recommended to the user. The paper presents method analysis of the various measures and matrices using a sample data set. It also compares the results of our model with the traditionally followed model. The proposed methodology promises to build a user-friendly recommendation system.

E-commerce Product Recommendation Based on Product Specification and Similarity

For many years, the use of recommender systems has increased and reached a new level recently. These systems are widely used in different areas, as they tend to offer and provide customers with the most suitable products or items that can satisfy their needs to the best. Especially in e-Learning environments, the purpose of such systems is to help students improve their learning by suggesting relevant resources and increase their knowledge through a selection of tips, hints, and tutorials. The process of recommendation considers the user’s profile which is a set of preferences and interests and is based on various techniques, often combined to provide improved results. However, due to the huge amount of data, the selection process becomes more and more difficult to handle and the same items get proposed repeatedly. Traceability, on the other hand, can either mean the history or the composition of something. It is a collection of actors, actions, objects and most importantly, time. In this paper, we introduce the use and the benefits of traceability and discuss how it can enhance recommendations. We also propose a traceability based approach for recommendations in e-Learning platforms, by means of a chatbot.

Introducing a Traceability Based Recommendation Approach Using Chatbot for E-Learning Platforms

There are number of users and items in any type of recommender system There are numerous information on internet and so many visitors on websites which add some challenges for generating recommender system A recommender system extracts the user preferences or interests from the related data sets so there is low information overload Therefore, new recommendation system is required which will provide more quality recommendations for huge data sets So, for these types of issues we have discovered several techniques of recommendation techniques which are three types such as: Content-based filtering, Collaborative filtering and Hybrid filtering This paper also analyzes different algorithms in each type of recommender system

Urvi Panchal

Papers

Methods of recommender system: A review