Similarity with Diversity for Better Recommendations
Searching online holidays websites can return hotels or villas that are very similar in location, function, and quality. Similarity is important but introducing diversity in information retrieval can significantly improve the accuracy, relevance, efficiency and effectiveness of these searches, particularly when queries are vague. Search result diversity is important as well as query similarity. When queries are vague, results should be similar to the query while at the same time being different from one another. Result diversity can provide the user with a representative sample of relevant items, rather than an incorrectly biased set of mutually similar results.
Query with Confidence
Verify and optimised business processes
Business process modelling is a technique for representing existing or future processes within an organisation, particularly with a view to documenting and improving them. Many business processes manipulate sophisticated data sources to generate a data set that will be subject to subsequent data analytics tasks, and their study falls within the general field of data management. User-friendly editors are available which allow business users to define business processes as a structured collection of tasks towards a specific goal. What they do not provide, however, is the ability to verify and optimise those processes or the data that they produce.
A concise review of customer history and other relevant information points
As the voice and sometimes face of a company, Contact Centre Agents must deal with customer inquiries efficiently and professionally. Pressures on agents include throughput-based service level goals as well as the need to ensure a top-quality service and sales experiences to customers at every point. Maintaining this quality and efficiency requires the provision of key customer history data to agents during calls without requiring agents to engage in time consuming searches across interfaces. Unfortunately the provision of such customer and case histories to the agent is far from a trivial task. Customer history summarisation is made difficult not only by enterprise-wide information integration challenges, but also by the computationally demanding task of determining the most relevant information that can be provided to the agent in bite-sized chunks.
Encouraging the greatest level of change in customer or user behaviour
Providing analytics-driven insight does not guarantee that people will take note of it and change their decision-making behaviours. For example, did a customer identified as a churn risk respond to the intervention recommended; or did loan officers in a bank actually base their lending decisions on the output of risk models? Measuring the success of any analytics-driven project, therefore, requires looking beyond the insights that are produced to ways in which these insights are communicated and delivered to ensure that behavioural change takes place. This research will benefit any company that wishes to make sure that the application of analytics results produces the required or expected outcome. For example, did a customer identified by a supermarket as a user of baby products take advantage of a promotion on nappies that was on offer that week? If not, can a message be tailored for that customer to encourage them to take advantage of such a promotion the next time? Using tailored messages, our research is working on encouraging the greatest level of change in a customer/user’s behaviour .
Continuous Data Stream Analytics for Image Data
Object detection, especially small object detection, is a well known and studied image analysis challenge. An example of which is finding predefined brands/logos in a large number of images or a continuous image stream in real-time. Current logo detection applications are generally run in an offline way, and cannot handle a large, continuous image data stream and process it in real time or near real-time.
Imaging Muscle Fibres
Audience Analytics for News
Make Better Online Purchasing Decisions
Online retailers such as Amazon and NewEgg, or travel sites such as TripAdvisor or Expedia, have large datasets of reviews on products or services. These reviews contain key information in relation to features and their performance, which can be used by a person or business to inform a purchase. However, there is currently no easy way to corral this rich but myriad data from numerous reviews on a product or service, potentially sourced across several online companies, into a form where a meta-review can be generated which might eventually inform a purchase.
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