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‘Adopt a consumer focus’ is the mantra that pervades the commercialisation strategies of horticultural products world-wide, but does this translate into practice in small and medium enterprises or is the process still production driven? Typically, new products in floriculture are modifications of existing products, which are introduced to existing markets, where consumers’ needs are well understood. Under these circumstances, the traditional role of market research is marginalised. In contrast, the commercialisation of ‘true’ new products into new markets involves a greater effort. Here, market research can identify market segments that are more receptive to innovation and experimentation. In this paper, the authors draw upon preliminary research and their initial experiences in the commercialisation of an Australian native flower to examine the inter-play of information flows and new product development.

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In multimedia retrieval, a query is typically interactively refined towards the ‘optimal’ answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. In this paper, we introduce a new approach called SaveRF (Save random accesses in Relevance Feedback) for iterative relevance feedback search. SaveRF predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses can be saved, hence reducing the number of random accesses. In addition, efficient scan on the overlap before the search starts also tightens the search space with smaller pruning radius. We implemented SaveRF and our experimental study on real life data sets show that it can reduce the I/O cost significantly.