Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

How TripAdvisor’s reviewers level of expertise influence their online rating behaviour and the usefulness of reviews

The internet has improved the buying behaviour of customers. The development of technologies has led to the dissemination of opinions on social networks where customers buy goods and services. These comments on social networks started to be a part of the purchasing process. Until a few years ago, customers used to choose their itineraries based on tourist guides or brochures. Nowadays, customers’ reviews have changed the way a destination is portrayed, enhancing the description of a product or a service to a level that not even the supplier was able to reach before. There are different types of reviewers. The aim of this study is to identify both reviews, experts and non-expert reviewers and analyse the way they write their reviews. Reviews of five hotels taken from the TripAdvisor website were used in order to conduct this study. After analyzing a great set of variables, the results show that there is not much different on the amount of positive/negative reviews written by a reviewer, however, there is a difference in the deeper meaning of a review when it is positive than when it is negative. The expert reviewer tends to be more emotional when writing positive reviews than negative reviews. Regarding the usefulness of the reviews, there is no significant difference in usefulness of a review whether is an written by an expert reviewer or by a non-expert reviewer. The results also indicate that being an expert does not influence the rating a reviewer gives to a hotel stay either. The study was conducted by using Lexalytics program to analyze a Natural Language Processing (NLP) used to classify reviews according to their polarity. With this study, a new research in study was filled. This study gives insights on the polarity of a review depending on the type of reviewer. The results of this study are also important for hotel managers in order for them to understand the type of guest in house.O desenvolvimento da tecnologia, com ênfase na internet e nos seus desenvolvimentos ao longo dos anos, melhorou o comportamento dos clientes e levou à disseminação de opiniões em redes sociais onde os clientes compram productos e serviços. Os comentários feitos a um produto ou serviço nas redes sociais começaram a fazer parte do processo da compra. Até há uns anos atrás, os clientes escolhiam os itinerários para as suas viagens com base em guias turísticos e brochuras. Recentemente, os comentários de clientes mudaram a maneira que um destino é explicado e ilustrado, melhorando, desta forma, a descrição de um produto/serviço a um nível que nem mesmo os fornecedores destes tinham alcançado ainda. Há diferentes tipos de reviewers. O objectivo deste estudo é identificar ambos tipos, expert e non-expert e analisar o estilo de reviews escrita por estes. Experts são assim denominados se tiverem escrito mais de dez reviews; por outro lado os non-expert reviewers são assim denominados se tiverem escrito menos de 10 reviews. Para este estudo, foi utilizada informação de cinco hotéis de Orlando, Florida, retirada do TripAdvisor. Depois de uma análise das variáveis, os resultados mostram que não há grande diferença no que toca ao volume de comentários positivos/negativos escritos por um utilizador. Por outro lado, existe uma diferença na emoção dada a cada comentário, entre os utilizadores. O expert reviewer tende a ser mais emocional quando escreve comentários positivos do que quando escreve comentários negativos. Relativamente a utilidade de cada comentário, não há grande diferença no que toca a ser um expert reviewer ou um non-expert a escrever um comentário. Os resultados indicam, também, que ser um expert não tem qualquer influência na avaliação que um utilizador dá a sua estadia num hotel. Este estudo foi feito com base no programa Lexalytics, com objectivo de analisar a Natural Language Processing (NLP) usada para classificar os comentários de acordo com a sua polaridade

Sentiment-Based Semantic Rule Learning for Improved Product Recommendations

Crucial data like product features and opinions that are obtained from consumer online reviews are annotated with the concepts of product review opinion ontology (PROO). The ontology with instance data serves as background knowledge to learn rule-based sentiments that are expressed on product features. These semantic rules are learned on both taxonomical and nontaxonomical relations available in PROO ontology. These rule-based sentiments provide important information of utilizing the relationship among the product features ‘as-a-unit’ to improve the sentiments of the parent features. These parent features are present at the higher level near the root of the ontology. The sentiments of the related product features are also improved. This approach improves the sentiments of the parent features and the related features that eventually improve the aggregated sentiment of the product. The result is either the change in the position of the product in the list of similar products recommended or appears in the recommended list. This helps the user to make correct purchase decisions

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors

Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202

A NOVEL FRAMEWORK BASED ON WORD-OF-MOUTH MINING FOR NON-PROSUMER DECISION SUPPORT

The deeper penetration of business-to-consumer e-commerce requires that customer decision support systems (CDSS) serve a wider range of users. However, a significant weakness of existing e-shopping assistance programs is their inability to aid non-professional consumers (non-prosumers) in buying highly differentiated products. This paper proposes a novel framework that infers product recommendations with minimal information input. At the heart of the proposed framework is the feature-usage map (FUM), a Bayesian network-based model that encodes the correlations among a product’s technical specifications and its suitability in terms of its using scenario (usage). It also incorporates a query-based lazy learning mechanism that elicits a product’s rating score from product reviews and constructs its corresponding FUM in an on-demand manner. This mechanism allows the knowledge base to be enriched incrementally, with no need for an exhaustive repository of FUMs pertaining to all possible usage queries a user may invoke. The effectiveness of the proposed framework is evaluated through an empirical user study. The results show that the framework is able to effectively derive product ratings based on specified usage. Moreover, this rating information can also be incorporated into a conventional buying guide system to deliver purchase decision support for non-prosumer