Review Bomb

This chapter provides a comprehensive overview of the phenomenon of review bomb, which occurs when an abnormally large amount of information is submitted to a rating system in a very short period of time by an overtly anonymous mass of accounts, with the overall goal of sabotaging the system's proper functioning. Because review bombs are frequently outbursts of social distress from gaming communities, gamification theories have proven useful for understanding the behavioral traits and conflict dynamics associated with such a phenomenon. A prominent case is analysed quantitatively. The methodology is discussed and proposed as a generalized framework for descriptive quantification of review bombs. As a result of the study, considerations for technological improvements in the collection of rating data in systems are proposed too.

Research on Design and Application of National Fitness System

In order to improve the physical quality of the national people, a national fitness system is designed and applied to practice. Design the overall architecture of the national fitness system, including the perception layer, network layer, and application layer. The perception layer mainly uses Internet of Things gateway, central machine, wireless perception node, and fitness data dashboard to obtain fitness data. The network layer mainly uses WiFi, 4G, Ethernet, and other public networks to transmit fitness data, fitness guidance data, and equipment operation and maintenance data. The application layer provides data storage, device management, user management, and client services. On this basis, through the collection of users’ fitness data rating data, the data are transformed into fitness data rating matrix, and the matrix is analyzed and calculated to realize the intelligent recommendation of fitness data and complete the design of national fitness data recommendation algorithm. The test results show that the system can meet the requirements of normal use, good compatibility, and user score is high and has high practical application value.

ESG Performance and Stock Price Volatility in Public Health Crisis: Evidence from COVID-19 Pandemic

Unlike traditional financial crises, COVID-19 is a global public health crisis with a significant negative impact on the global economy. Meanwhile, the stock market has been hit hard, and corporate share prices have become more volatile. However, the stock prices of some enterprises with good performance of ESG (Environment, Social, and Governance) are relatively stable in the epidemic. This paper selects ESG rating data from MSCI (Morgan Stanley Capital International) with better differentiation, adopts multiple regression and dummy variables, and adopts the Differences-in-Differences (DID)model with the help of COVID-19, an exogenous event. Empirical test the impact of ESG performance on the company’s stock price fluctuations. The results show that the stock price volatility of companies with good ESG performance is lower than that of companies with poor performance. Second, COVID-19 exacerbates volatility in company stock prices, but the increase in stock price volatility of companies with good ESG performance is small. That is, good ESG performance helps reduce the increase in stock price volatility due to COVID-19 shock, and plays a role in enhancing “resilience” and stabilizing stock prices. This paper provides new empirical evidence for the study of ESG performance and corporate stock price volatility, and puts forward relevant policy recommendations for enterprises and government departments.

Enhancing rating prediction for recommendation by metric learning with gray relational analysis

PurposeThe purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.Design/methodology/approachFirst, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.FindingsThe novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.Practical implicationsThe rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.Originality/valueThe algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

A novel user review-based contextual recommender system

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.

Perceptions of Local Inhabitants towards Land Management Systems Used in the Rainforest Area of Ecuador: An Evaluation Based on Visual Rating of the Main Land Use Types

Land management policy and practice affects a wide segment of stakeholders, including the general population of a given area. This study evaluates the perceptions of local inhabitants towards the land management systems used in the rainforest area of Ecuador—namely, unmanaged (natural) forest, managed forest, croplands, and pasturelands. Data collected as ratings on 12 pictures were used to check the aggregated perceptions by developing the relative frequencies of ratings, in order to see how the perception rating data were associated with the types of land management systems depicted by the pictures, and to see whether the four types of land management could be mathematically represented by a clustering solution. A distinctive result was that the natural forests were the most positively rated, while the managed forests were the least positively rated among the respondents. It seems, however, that human intervention was not the landscape-related factor affecting this perception, since croplands and pasturelands also received high ratings. The ratings generated a clear clustering solution only in the case of forest management, indicating three groups: natural forests, managed forests, and the rest of the land management systems. Based on the results of this study, a combination of the four land use systems would balance the expectations of different stakeholders from the area, while also being consistent to some extent with the current diversity in land management systems. However, a more developed system of information propagation would be beneficial to educate the local population with regards to the benefits and drawbacks of different types of land management systems and their distribution.

A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.

Implicit Feedback Recommendation Method Based on User-Generated Content

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

Occupation-congruent appearance: a measuremental approach

In this article we introduce a novel measure, which we call ‘occupation-congruent appearance’ (OCA). We argue that the measure captures the appearance norms of looking ‘right’ for a particular occupation. Using a combination of large-scale photograph data (N=1,411) and rating data (N=7,920) from Finland, including 387,542 individual ratings, we show that shared cultural standards for OCA exist, and rate of agreement compares with agreement on beauty standards. We systematically compare the relationship between OCA, attractiveness, and masculinity/femininity in male-dominated, gender-balanced, and female-dominated occupational fields for men and for women. We conclude that occupation-congruent appearance is independent from other typically used measures in studies on appearance and social inequalities. Thus, it seems that OCA can capture the kind of elements of appearance that are not reducible to attractiveness, femininity, and masculinity. We discuss the possibilities for using OCA as a complementary measure for researchers interested in appearance and social inequalities.

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.