Review-guided Helpful Answer Identification in E-commerce

Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.Comment: Accepted by WWW202

Quality of E-Commerce Practices in European Enterprises: Cluster Analysis Approach

Development and usage of e-Commerce has a positive impact on business, while it offers better services to clients and higher profitability for enterprises. Easier and effective communication processes between sellers and buyers provides an additional benefit for further expansion of e-Commerce especially in developing countries. The goal of the article is to determine the quality and usage of e-Commerce practice in enterprises with 10 or more employees in European countries in 2018. The analysis will be conducted using hierarchical cluster analysis aiming to investigate whether there are significant differences among selected European countries regarding e-Commerce practices. Research results indicate that selected European countries can be divided into four homogeneous groups with similar characteristics regarding e-Commerce practices. According to the results of this study, e-Commerce practices are more often used in European countries with a more developed ICT sector such as Belgium and Ireland

“I found this law firm on Google Reviews, and I wasn’t disappointed”: A Linguistic Study of Positive Law Firm Client Reviews and Business Owner Responses in the US and the UK

This paper proposes a corpus-assisted discourse analysis of positive online reviews of US and UK law firms and the corresponding business owners’ responses. The aim is to better understand and describe their linguistic nature and their structure, and assess whether and to what extent cross-cultural variation occurs. The methodology used to carry out the analysis combined different approaches to linguistic inquiry. It started with a lexico-grammatical description of reviews and responses through corpus methods, then continued with a move structure analysis that focused on rhetorical differences between British and American reviews and responses and ended with an attentive cross-cultural analysis on the usage of positive evaluative adjectives in reviews, conducted through the lens of appraisal theory. The cross-cultural analysis indicated that US reviews and responses are more oriented towards experience-sharing, recommendation, and publicity. By contrast, the UK’s samples are characterised by a more attentive expression of politeness and conversational rituals. Moreover, the adjectives used to express positive evaluations revealed an interesting tendency: that of foregrounding communication and social skills in US reviews and technical and professional skills in UK reviews

Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.Comment: Accepted by SIGIR 202