相关论文: Positivity Bias in Customer Satisfaction Ratings

Large language models (LLMs) with chat-based capabilities, such as ChatGPT, are widely used in various workflows. However, due to a limited understanding of these large-scale models, users struggle to use this technology and experience…

Network operators need to continuosly upgrade their infrastructures in order to keep their customer satisfaction levels high. Crowdsourcing-based approaches are generally adopted, where customers are directly asked to answer surveys about…

Many businesses depend on their mobile apps and websites, so user frustration while trying to complete a task on these channels can cause lost sales and complaints. In this research, I use clickstream data from a real e-commerce site to…

Prior work on sentiment analysis using weak supervision primarily focuses on different reviews such as movies (IMDB), restaurants (Yelp), products (Amazon).~One under-explored field in this regard is customer chat data for a customer-agent…

The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has concentrated on…

Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of negation in customer service interactions, particularly applied to sentiment…

In social commerce, users dialogue with each other on the topics related to the providers' products. However, the language customers use may vary from the language vendors use on their e-commerce websites and product descriptions. This…

Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational…

A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased…

Many online platforms predominantly rank items by predicted user engagement. We believe that there is much unrealized potential in including non-engagement signals, which can improve outcomes both for platforms and for society as a whole.…

In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scenario in which a company sells a product and can observe only…

Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unlabeled data are regarded as negative data in existing…

People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this…

Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormalities). When positives are rare, the prevalence effect induces systematic cognitive biases…

Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that…

Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based…

The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for…

Fairness in recommender systems has recently received attention from researchers. Unfair recommendations have negative impact on the effectiveness of recommender systems as it may degrade users' satisfaction, loyalty, and at worst, it can…

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment…

With the increasing popularity of conversational search, how to evaluate the performance of conversational search systems has become an important question in the IR community. Existing works on conversational search evaluation can mainly be…