20,688 research outputs found
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
Application of Natural Language Processing to Determine User Satisfaction in Public Services
Research on customer satisfaction has increased substantially in recent
years. However, the relative importance and relationships between different
determinants of satisfaction remains uncertain. Moreover, quantitative studies
to date tend to test for significance of pre-determined factors thought to have
an influence with no scalable means to identify other causes of user
satisfaction. The gaps in knowledge make it difficult to use available
knowledge on user preference for public service improvement. Meanwhile, digital
technology development has enabled new methods to collect user feedback, for
example through online forums where users can comment freely on their
experience. New tools are needed to analyze large volumes of such feedback. Use
of topic models is proposed as a feasible solution to aggregate open-ended user
opinions that can be easily deployed in the public sector. Generated insights
can contribute to a more inclusive decision-making process in public service
provision. This novel methodological approach is applied to a case of service
reviews of publicly-funded primary care practices in England. Findings from the
analysis of 145,000 reviews covering almost 7,700 primary care centers indicate
that the quality of interactions with staff and bureaucratic exigencies are the
key issues driving user satisfaction across England
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizationsā business strategy development and individual consumersā comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection
The authors address two novel and signiļ¬cant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selectionāreviewers choose the subset of attributes to write aboutāin metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they ļ¬nd that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the āhardā sentiment classiļ¬cation problems. Further, accounting for attribute self-selection signiļ¬cantly impacts sentiment scores, especially on attributes that are frequently missing
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