NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2)

How to deal with negative preferences in recommender systems: a theoretical framework

Negative information plays an important role in the way we express our preferences and desires. However, it has not received the same attention as positive feedback in recommender systems. Here we show how negative user preferences can be exploited to generate recommendations. We rely on a logical semantics for the recommendation process introduced in a previous paper and this allows us to single out three main conceptual approaches, as well as a set of variations, for dealing with negative user preferences. The formal framework provides a common ground for analysis and comparison. In addition, we show how existing approaches to recommendation correspond to alternatives in our framework.

Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning

Understanding the customer behavior and perception are important issues for motivating customer satisfaction in marketing analysis. Customer conversation with customer support services through social networks channel provides a wealth of information for understanding customer perception. Therefore, in this paper, a hybrid framework that integrated sentiment analysis and machine learning techniques is developed to analyze interactive conversations among customers and service providers in order to identify the change of polarity of such conversation. This framework aims to detect the conversation polarity switch as well as predict the sentiment of the end of the customer conversation with the service provider. This would help companies to improve customer satisfaction and enhance the customer engagement. The effectiveness of the proposed framework is measured by extracting a real dataset that expresses more than 5000 conversational threads between a customer service agent of an online retail service provider (AmazonHelp) and different customers using the retailer’s twitter public account for the duration of one month. Different classical and ensemble machine learning classifiers were applied, and the results showed that the decision trees outperformed all other techniques.

A data-driven approach to clinical decision support in tinnitus retraining therapy

Background

Tinnitus, known as “ringing in the ears”, is a widespread and frequently disabling hearing disorder. No pharmacological treatment exists, but clinical management techniques, such as tinnitus retraining therapy (TRT), prove effective in helping patients. Although effective, TRT is not widely offered, due to scarcity of expertise and complexity because of a high level of personalization. Within this study, a data-driven clinical decision support tool is proposed to guide clinicians in the delivery of TRT.

Methods

This research proposes the formulation of data analytics models, based on supervised machine learning (ML) techniques, such as classification models and decision rules for diagnosis, and action rules for treatment to support the delivery of TRT. A knowledge-based framework for clinical decision support system (CDSS) is proposed as a UI-based Java application with embedded WEKA predictive models and Java Expert System Shell (JESS) rule engine with a pattern-matching algorithm for inference (Rete). The knowledge base is evaluated by the accuracy, coverage, and explainability of diagnostics predictions and treatment recommendations.

Results

The ML methods were applied to a clinical dataset of tinnitus patients from the Tinnitus and Hyperacusis Center at Emory University School of Medicine, which describes 555 patients and 3,000 visits. The validated ML classification models for diagnosis and rules: association and actionable treatment patterns were embedded into the knowledge base of CDSS. The CDSS prototype was tested for accuracy and explainability of the decision support, with preliminary testing resulting in an average of 80% accuracy, satisfactory coverage, and explainability.

Conclusions

The outcome is a validated prototype CDS system that is expected to facilitate the TRT practice.

A new knowledge discovery approach for mining business trade barriers

Cross-border trade barriers introduced by national authorities to protect local business and labor force cause substantial damage to international economical actors. Therefore, identifying such barriers beyond regulator’s audit reporting is of paramount importance. This paper contributes towards this goal by proposing a novel approach that uses natural language processing and deep learning method for uncovering Finnish-Russian trade barriers in the fish industry from selected business discussion forums. Especially, the approach makes use i) a three-leg ontology for data collection, ii) a BERT architecture for mapping Onkivisit-Shaw-Kananen trade barrier ontology to negative polarity posts and, iii) a new reverse-engineering clustering approach to identify the causes of individual trade-barrier types. A comparison with official statistical reports has been carried out to identify the salient aspects of trade-barriers that hold regardless of the time difference. The findings reveal the dominance of the Time-length barrier type in the Finnish discussion forum dataset and import vs export tariff discrepancy and product requirement barrier types in the Russian forum dataset. The developed framework can serve as a tool to assist companies or regulators in providing business-related recommendations to overcome the detected trade barriers.