Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

OUPIP

This article aims to propose an ontological user model called OUPIP (Ontology-Based User Profile for Impairment Person), that extends existing ontologies to help designers and developers to adapt applications and devices according to the user's profile, disability and dynamic context. Besides, the approach has been applied in a typical real-life scenario in which personalized services are provided to impairment person through a mobile phone.

An illegal user model of communication channel with error correction codeс

The situation when an illegal user intercepts data from the communication channel of two legal users is considered. The data in the legal channel is protected from distortion by error correction codes. It is assumed that the intercept channel may be noisier than the legal channel. And, consequently, the interceptor receives low-quality data. The possibility of using a special type of error-correction code decoders by an illegal user to restore damaged data is discussed. The interceptor model is constructed. The model includes a receiving block of the intercept channel, as well as several auxiliary blocks, such as a memory device, a block for determining the quality of communication in the legal channel, databases with possible decoders and their parameters. Examples are given that show the potential capabilities of the interceptor. The examples demonstrate the difference in decoding quality between different decoders for LDPC codes and Reed-Muller codes. These examples show that an illegal user with the various decoders described in the open press can receive information with satisfactory quality even at a weak signal level in the intercept channel. The constructed model can be useful in the tasks of developing methods of protection against intruders who organize illegitimate data interception channels.

A bottom-up method for remixing narratives for virtual heritage experiences

Considering the impacts COVID-19 has had on travel and many economies, developing virtual experiences that are well-received by different publics has become even more prominent. This paper shows how a multimodal discourse analysis can be used to as a bottom-up approach to identifying narrative themes that can be used in virtual experiences for cultural heritage sites. A case study on 11 UNESCO World Heritage Australian Convict Sites shows how diverse sources of user-generated content, tourism marketing materials and historical information can be analysed and then remixed into a virtual tour of the sites in the form of an interactive web documentary (iDoc). Although this case study involved a total of seven narrative development phases, this paper focuses on two phases, namely how the user model and content model were determined. These models were later used to develop the resulting iDoc prototype. The user model focused on the prospective audience of cultural heritage tourists, and a content model of narrative themes for the iDoc was developed through a multimodal discourse analysis. This bottom-up approach of analysing existing cultural data allows for the discovery of the prospective audiences’ interests as well as narrative themes that can be included in virtual heritage experiences. It also provides a new creative methodology that can prevent issues that may arise with top-down narratives that focus too heavily on one institutional perspective or national narrative and lack direct engagement with or understanding of today’s publics.

How Am I Doing?: Evaluating Conversational Search Systems Offline

As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important respects: conversational search systems are less likely to return ranked lists of results (a SERP), more likely to involve iterated interactions, and more likely to feature longer, well-formed user queries in the form of natural language questions. Because of these differences, traditional methods for search evaluation (such as the Cranfield paradigm) do not translate easily to conversational search. In this work, we propose a framework for offline evaluation of conversational search, which includes a methodology for creating test collections with relevance judgments, an evaluation measure based on a user interaction model, and an approach to collecting user interaction data to train the model. The framework is based on the idea of “subtopics”, often used to model novelty and diversity in search and recommendation, and the user model is similar to the geometric browsing model introduced by RBP and used in ERR. As far as we know, this is the first work to combine these ideas into a comprehensive framework for offline evaluation of conversational search.

Interacting with Explanations through Critiquing

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations.

Implementation of Fraudulent Sellers Detection System of Online Marketplaces using Machine Learning Techniques

The E-commerce proportion in global retail expenditure has been steadily increasing over the years showing an obvious shift from brick and mortar to retail clicks. To analyze the exact problem of building an interactive models for the identification of auction fraud in the entry of data into ecommerce. This is why the most popular site's business develops with retailers and other auction customers. Where viral customers purchase products from online trading, customers may worry about fraudulent actions to get unlawful benefits from honest parties. Proactive modesty systems for detecting fraud are thus a necessary practice to prevent such illegal activities. The shopping product is built according to the customer's requirements and is safer online and resting, and the rules and regulations that are necessary to follow no longer seem to be the best of workable selection, coefficient limits that facilitate the shopping product and make it easier for the user model to compete on each platform so that it can experiment.

HUMAN INTELLIGENCE ANALYSIS AND DETECTING SCENARIOS USING ARTIFICIAL INTELLIGENCE BASED ON HUMAN COMPUTER INTERACTION IN EDUCATION SYSTEM

It is different from the situation that people have just experienced normal information from general websites. Users of an educational web application for teaching as faculty and students need more communication with each other. That means they desperately need to communicate with mediators like computers and other resources. Therefore, research conducted on the design of human computers takes a huge amount of value. In this paper, a user model of Educational Web Application is concluded. The Web Application includes that Facial Recognition for login, attendance and to see the student’s behavior in the classroom by continuous monitoring. Knowledge trees and tests are designed for better understanding and to improve yourself in particular or all subjects. The aim of the paper is to develop a system to manage and maintain sentiments and detecting student’s behavior by humancomputer interaction. We propose a software framework to monitor boundaries, analyze them and support users to see how they interact in conversations or lectures and manage their emotions.

Grounded Theory for the Design of Mobile User Interfaces-Based on Space Retrieval Therapy

<span lang="EN-US">Technology plays a major role in our daily lives. In healthcare, technology assists in treating and detecting diseases and can improve patients’ quality of life. Alzheimer’s disease patients are generally elderly people who suffer from disabilities in vision, hearing, speech, and movement. The disease is one of the most common types of dementia. This paper proposes a design for a mobile application with an adaptive user interface for Alzheimer’s patients based on an elderly model developed using grounded theory. The application aims to improve the patients’ quality of life and allow them to remain engaged in society. The design of the application is based on spaced retrieval therapy (SRT), a method that helps Alzheimer’s patients to recall specific pieces of important information. User-centered design method was used to design and build this application. In the requirements phase, a user model for elderly people was elicited based on a classification developed through grounded theory. The prototype for the proposed model was designed and developed considering the default user interfaces and the adaptive user interfaces. A test was conducted with 15 elderly Arab users. The participants were 50–74 years old with varying levels of education and experience with technology. The authors proposed a user model for elderly people containing all the design implications in terms of physical and cognitive changes. The results of testing with elderly Arab users supported the proposed user model in terms of colors, fonts, pictures, and symbols. However, there were problems with the menu design and color preferences.</span>