Overcome Information Overload Research Articles

Towards a News Recommendation System to increase Reader Engagement through Newsletter Content Personalization

In the big data era, recommendation systems (RS) play a pivotal role to overcome information overload. In the digital landscape publishers need to optimize their editorial strategies to increase reader engagement and digital revenue. Newsletters emerged as an important conversion channel to engage readers as they provide a personalized experience by building habits. However, the lack of human resources and the need for more content assertiveness per reader lead publishers to search for an advanced analytics solution. We address this problem by proposing a research agenda on news recommendation algorithms inspired in the table d’hôte approach and the concept of ‘personalized diversity’. Thus, the reader receives a personalized newsletter where he can discover informative and surprising content. The goal is to offer a self-contained package that retains readers, increases loyalty and consequently, the propensity to subscribe. A live controlled experiment with readers from the Portuguese newspaper Público was performed and a new approach is proposed. We study the effects of content recommendations on the behavior of newsletter subscribers. Findings reveal that serendipitous content tends to increase reader engagement. Finally, we propose a table d’hôte approach and new challenges are identified for future research.

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IoT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

Self-Attentional Multi-Field Features Representation and Interaction Learning for Person–Job Fit

Person–job fit, which aims to predict the matching degree between a resume and a job, has become an effective way to overcome information overload in the recruitment market. Existing studies on person–job fit usually focus on the representation learning of textual data in jobs and resumes. Person–job fit is a highly nonlinear complex problem that is affected by several fields of features. We assume that it would bring benefits to comprehensively consider the numerical features, categorical features, and textual features of resumes and jobs. To this end, we propose a novel model based on the self-attention mechanism, named MUlti-Field Features representation and INteraction (MUFFIN) learning for person–job fit. The key idea is to explore meaningful feature representations and interactions. Specifically, we group all the features of resumes and jobs into several fields. And a module is introduced to learn the hidden vectors of feature correlations in each feature field. Along this line, we propose a module with the multi-head self-attention mechanism and a residual connection to further model the feature field interactions. Moreover, we utilize a multi-layer perceptron (MLP) to measure the matching score between a resume and a job. Finally, the experimental results on a real-world dataset validate the effectiveness of MUFFIN for person–job fit.

Content Based Movie Recommendation System

Abstract: Recommender System is a tool which helps users find the required content and overcome information overload. It predicts interests of users by using Machine Learning algorithms and makes recommendation according to the interest of users. The primary content-based recommender system is the continuation and development of collaborative filtering, which does not need the user’s appraisal for items. Instead, the similarity is calculated based on the data of items that are selected by users, and then make the recommendation appropriately. With the augmentation of machine learning, the current content-based recommender system can build profile for users and products respectively. Building or renewing the profile according to the perusal of items that are bought or seen by users. The system can differentiate the user and the profile of items and then recommend the most resembling products. So, this recommender method that compel user and product directly can’t be brought into collaborative filtering model. The groundwork of content-based algorithm is acquisition and quantitative analysis of the content. The research of acquisition and filtering of text information are fully fledged, many current modified content-based recommender systems make recommendations according to the analysis of text data. This paper introduces content-based recommendation system for the movie websites. There are a lot of factors extracted from the movie, they are diverse and unique, which is also different from other recommender systems. We use these aspects to construct movie model and calculate similarity. We introduce a new outlook for setting weight of features, which improvises the representation of movie recommendations. Finally, we evaluate the approach to illustrate the improvement

DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgement that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, Dr.Bench, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models for diagnostic reasoning. The goal of DR. BENCH is to advance the science in cNLP to support downstream applications in computerized diagnostic decision support and improve the efficiency and accuracy of healthcare providers during patient care. We fine-tune and evaluate the state-of-the-art generative models on DR.BENCH. Experiments show that with domain adaptation pre-training on medical knowledge, the model demonstrated opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community. We also discuss the carbon footprint produced during the experiments and encourage future work on DR.BENCH to report the carbon footprint.

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.

Recommendation System Issues, Approaches and Challenges Based on User Reviews

With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users’ trust.

A Survey on Stream-Based Recommender Systems

Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS). In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.

Integrating contextual sentiment analysis in collaborative recommender systems.

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

An Overview of Recommendation Techniques and Their Applications in Healthcare

With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, collaborative filtering (CF)-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

The ethics of scientific recommender systems

Scientific recommender systems have become increasingly popular as a tool to overcome information overload, allowing researchers to access fresh and relevant content. However, this article presents an analysis of the most pressing ethical challenges posed by recommender systems in the context of scientific research. In particular, it is argued that scientific recommender systems may risk isolating scholars in information bubbles and insulating them from exposure to different viewpoints. Further, they also risk suffering from popularity biases which may lead to a winner-takes-all scenario and reinforce discrepancies in recognition received by eminent scientists and unknown researchers. The article concludes with recommendations for scientists, journals, and digital libraries to facilitate progress in the study of the ethics of scientific recommender systems.

Extracting core answers using the grey wolf optimizer in community question answering

A rapidly increasing number of users in community question answering has led to an explosive growth of answers. Thus, it is becoming increasingly more difficult to browse all the answers. Choosing a subset of answers arbitrarily will likely lead to cognitive bias and even poor decisions. Reading a set of core answers that can cover most topics of all the answers is a novel method to overcome information overload and facilitates information retrieval. In this paper, the method named AnsExt for extracting core answers using a kind of swarm intelligence algorithm, named the grey wolf optimizer (GWO), is proposed. First, answers are modeled with the biterm topic model, which fits both short and long texts. Then, factors including quality, coverage, redundancy and number in extracting core answers are defined. Two scenarios are modeled with the requirements of quality, coverage and redundancy: extracting the least number of answers and extracting a predefined number of answers. To extract the minimum number of core questions, a binary GWO is used to resolve the single-objective optimal model. The binary multi-objective GWO is constructed to resolve the optimal model, which is used to extract a predefined number of core answers. Extensive experiments are conducted on real datasets. The results show that the proposed method is feasible and performs well.

Pravni aspekti merčendajzinga u sportu

In the last thirty years, sport has undergone a significant transformation from a means of satisfying the need for physical activity, affirmation or competition with others to an extremely profitable industry. The development of sports has been favored by the development of mass media in terms of the creation of planetary sports idols. Consumers today are exposed to aggressive marketing policies, and manufacturers are trying to overcome information overload by exploiting recognizable symbols that consumers want to identify with through the emotional positioning of products. The authors point out the importance of merchandising as well as all the specificities that it manifests in its application in sports, with particular reference to the legal framework, and instruments for the further transfer of rights and protection of them.

Leverage side information for top‐N recommendation with latent Gaussian process

SummaryCurrently, Recommender Systems (RS) have been ubiquitously applied to various online applications and obtained tremendous success due to their capability to overcome information overload; however, the available Side Information (SI), such as demographics and attributions of items, is always neglected. Actually, SI could reflect user's interests and preference, and even influence user's decision over items; therefore, it would be greatly helpful to leverage side information to improve the performance of RS, which is already partly certificated in previous research studies. Motivated by this, in this article, a novel multi‐task learning‐based recommendation system referred to as LGP‐RS is proposed, which takes utility functions mapping from the non‐linear side information to low‐rank feature space with Gaussian process (GP) priors, for users and items, respectively. In addition, Laplace approximation method is employed to approximate the posterior distribution for the utility functions, and gradient‐based method is employed to learn the hyperparameters for GP. This flexible approach is able to capture the non‐linear high‐order interactions within side information, reduce the prediction uncertainty, and provide personalized top‐N recommendation. Experimental analysis over three real‐world datasets demonstrates that LGP‐RS could deal with both explicit and implicit side information simultaneously, and significantly outperforms state‐of‐the‐art approaches.

Helping consumers to overcome information overload with a diversified online review subset

Redundant online reviews often have a negative impact on the efficiency of consumers’ decision-making in their online shopping. A feasible solution for business analytics is to select a review subset from the original review corpus for consumers, which is called review selection. This study aims to address the diversified review selection problem, and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection (SA-DRS) that considers the semantic relationship of review features and the content diversity of selected reviews simultaneously. SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation (LDA) topic model and the Word2vec model to measure the topic relation and word context relation. Based on the established feature taxonomy, the similarity between each pair of reviews is defined and the review quality is estimated as well. Finally, diversified, high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method, forming the selected review subset. Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches.

Recommender System for Integrating Tacit Knowledge in E-learning Environment to Enhance Learning Potential of Learners

New technologies and fast expansion of internet have made access to information easier for learners in web-based education. E-learning environments today are used largely to handcraft training courses about thoughtfully selected topics for delivery to learners registered for those courses. E-learning environments are online so it is not mandatory for learners and instructors to meet in the same place and these e-learning environments play an important role in self-learning and self-training of learners. Educational organizations nowadays adapting e-learning platforms for their learners so as they can gradually get benefited and enhance their learning capabilities. The recommender system helps the learners to overcome information overload problem as learners are recommended e-learning resources according to their habits, likes & dislikes, learning styles, interest area and their level of knowledge.The paper presents a novel approach, a framework for building architecture for a recommender system that integrates tacit knowledge in e-learning environment so as to enhance the learning potential of learners. The proposed approach is based on Nonaka and Takeuchi’s knowledge based SECI model of learning that twirl around inter-transformation of two forms of knowledge that are explicit and tacit. The proposed framework adapts SECI model and contains four modules, first a learner module which contains learners’ personal information, their learning style and their interest area that is uses to identify learners learning preferences and activities. Second a domain module which contains all the information for a particular area and holds the knowledge about the set of course structure. Third an e-assessment module that consists of three blocks named diagnostic assessment, formative assessment and revision module, the main intention of is to provide more benefits to the learners to improve their knowledge. The last module is recommendation module that helps to generate the recommendation of suitable learning module to the learners, founded on their learning style and their level of knowledge.

Bayesian pairwise learning to rank via one-class collaborative filtering

With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user’s preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.

Deep Learning Based Recommender System

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.