A Framework for Feature Selection using Natural Language Processing for User Profile Learning for Recommendations of Healthcare Related Content

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.

Learning a Hierarchical Intent Model for Next-Item Recommendation

A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.

Semantic Term-Term Coupling based Feature Enhancement of User Profiles in Recommendation Systems

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.

Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection

Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on rumor detection, propose a dual co-attention-based multi-feature fusion method for rumor detection, and explore the detection capability of the proposed method in early rumor detection tasks. The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding . It simultaneously integrates features from three sources: publishing user profiles, source tweets, and comments. In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively. The bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence. A dual collaborative attention mechanism is used to explore the correlation among publishing user profiles, tweet content, and comments. Then the feature vector is fed into classifier to identify the implicit differences between rumor spreaders and non-rumor spreaders. In this study, we conducted several experiments on the Weibo and CED datasets collected from microblog. The results show that the proposed method achieves the state-of-the-art performance compared with baseline methods, which is 5.2% and 5% higher than the dEFEND. The F1 value is increased by 4.4% and 4%, respectively. In addition, this paper conducts research on early rumor detection tasks, which verifies the proposed method detects rumors more quickly and accurately than competitors.

Clustering Users to Determine the Most Suitable Gamification Elements

The use of gamification elements has extended from being a complement for a product to being integrated into multiple public services to motivate the user. The first drawback for service designers is choosing which gamification elements are appropriate for the intended audience, in addition to the possible incompatibilities between gamification elements. This work proposes a clustering technique that enables mapping different user profiles in relation to their preferred gamification elements. Additionally, by mapping the best cluster for each gamification element, it is possible to determine the preferred game genre. The article answered the following research questions: What is the relationship between the genre of the game and the element of gamification? Different user groups (profiles) for each gamification element? Results indicate that there are cases where the users are divided between those who agree or disagree. However, other elements present a great heterogeneity in the number of groups and the levels of agreement.

eHealth and mHealth Development in Spain: Promise or Reality?

In the last decades, the use of Information and Communication Technologies (ICTs) has progressively spread to society and public administration. Health is one of the areas in which the use of ICTs has more intensively developed through what is now known as eHealth. That area has recently included mHealth. Spanish health system has stood out as one of the benchmarks of this technological revolution. The development of ICTs applied to health, especially since the outbreak of the pandemic caused by SARS Cov-2, has increased the range of health services delivered through smartphones and the development of subsequent specialized apps. Based on the data of a Survey on Use and Attitudes regarding eHealth in Spain, the aim of this research was to conduct a comparative analysis of the different eHealth and mHealth user profiles. The results show that the user profile of eHealth an mHealth services in Spain is not in a majority. Weaknesses are detected both in the knowledge and use of eHealth services among the general population and in the usability or development of their mobile version. Smartphones can be a democratizing vector, as for now, access to eHealth services is only available to wealthy people, widening inequality.

Identifikasi Pengaruh Pandemi Covid-19 terhadap Perilaku Pengguna Twitter dengan Pendekatan Social Network Analysis

<p class="Abstrak">Pandemi COVID-19 yang berlangsung lama telah berdampak masif pada berbagai aktivitas publik, misalnya perilaku pengguna di media sosial. <em>Twitter</em>, media sosial yang fleksibel untuk berdiskusi dan bertukar pendapat, menjadi salah satu media populer dalam menyebarluaskan informasi COVID-19 secara dinamis dan <em>up-to-date</em>. Hal ini menjadikan <em>twitter</em> relevan sebagai media ekstraksi pengetahuan dalam mengidentifikasi perubahan perilaku pengguna. Kontribusi penelitian ini adalah menemukan perubahan perilaku pengguna <em>twitter</em> melalui analisis profil pengguna pada periode sebelum dan setelah COVID-19. Data yang digunakan adalah data <em>tweet</em> berbahasa Indonesia. Penelitian ini menggunakan pendekatan <em>Social Network Analysis</em> (SNA) sebagai ekstraksi informasi dalam menentukan aktor utama dan aktor populer. Kemudian, profil pengguna aktif dianalisis untuk mengidentifikasi perubahan perilaku melalui intensitas <em>tweet</em>, popularitas pengguna, dan representasi topik pembahasan. Popularitas pengguna dianalisis dengan pendekatan <em>follower rank</em>, sedangkan representasi topik pembahasan diekstraksi dengan metode <em>Latent Dirichlet Allocation</em> untuk mendapatkan dominan topik yang dibahas oleh setiap pengguna aktif. Tujuannya adalah untuk mempermudah identifikasi pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna <em>twitter</em>. Berdasarkan hasil SNA, penelitian ini menemukan tiga aktor kunci yang aktif pada periode sebelum dan setelah COVID-19. Selanjutnya, hasil analisis dari ketiga aktor tersebut menunjukkan adanya pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna <em>twitter</em>, yaitu kenaikan intensitas <em>tweet</em> sebesar 58% pada jam kerja, aktor utama yang didominasi oleh 60% pengguna dengan <em>follower</em> rendah, dan topik pembicaraan pengguna twitter yang dominan membahas COVID-19, hobi dan aktivitas di dalam rumah.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><strong><br /></strong>The long-lasting COVID-19 pandemic had a massive impact on public activities, such as user behavior on social media. Twitter, a flexible social media for discussing and exchanging opinions, has become popular in disseminating COVID-19 dynamic and up-to-date information. It makes twitter relevant as a medium of knowledge extraction in identifying user behavior changes. The contribution of this research is to find behavior changes of Twitter users through user profiles analysis in the before and after COVID-19 period. This data used is Indonesian-language tweets. This research used a Social Network Analysis (SNA) to determine the main actors and famous actors. Then, active user profiles were analyzed to identify behavior changes through tweet intensity, user popularity, and representation of the topic of discussion. User popularity was analyzed using a follower rank approach. At the same time, the representation of discussion topics was extracted using the Latent Dirichlet Allocation method to obtain dominant topics which each active user discusses. It aims to make it easier to identify the impact of the COVID-19 pandemic on Twitter user behavior changes. Based on the results of the SNA, this research found three key actors who were active in the before and after COVID-19 period. Then, the results of the analysis of these three user profiles shows that an influence of the COVID-19 pandemic on Twitter user behavior changes: an increase in tweet intensity by 58% during working hours, the leading actor was dominated by 60% of users with low followers, and the topic of Twitter users' conversation that it dominantly discuss COVID-19 issues, hobbies, and activities at home.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

What colour are you? Smartphone addiction traffic lights and user profiles

PurposeThe key concern nowadays is smartphone addiction and user profiles. Following the risk and protective factors framework, the authors aim to characterize smartphone users according to two levels: (1) individual: referred to the use (i.e. boredom proneness, compulsive app downloading smartphone addiction) and (2) microsystem: referred to family and peers (i.e. family harmony and phubbing). Besides, the authors will derive useful managerial implications and strategies.Design/methodology/approachFirst, an extensive literature revision and in-depth interviews with experts were employed to identify the addiction-related variables at the individual and microsystem level. Second, information was collected from a sample of 275 Spanish smartphone users, and a K-means clustering algorithm was employed to classify smartphone users.FindingsThe proposed traffic lights schema identifies three users’ profiles (red, yellow and green) regarding their smartphone addiction and considering individual and microsystem critical variables.Originality/valueThis study proposes a practical and pioneer traffic lights schema to classify smartphone users and facilitate each cluster's strategies development.