Browsing Conference Presentations by Title
Now showing items 986-1005 of 1704
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Negotiation models(Springer, 2007)
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New concepts, new practices - The business implications of sustainable development as a new paradigm(2012)Religious organisations are major investors with sometimes substantial investment volumes. An important question for them is how to make investments in, and to earn returns from, companies and activities that are consistent with their religious beliefs or that even support these beliefs. Religious organisations have pioneered responsible investment. Yet little is known about their investment attitudes. This article addresses this gap by studying faith consistent investing. Based on a survey complemented by interviews, we investigate religious organisations’ attitudes towards responsible investment including opinions, practices and the impediments for implementing faith consistent investing. Although our results cannot be generalised because of the non-random character of our sample, six main characteristics of faith consistent investing are drawn: investing is not perceived as being in contradiction with religious values, religious values are important drivers, there is a strong community around faith consistent investing, religious investors are pioneering impact investing, implementing faith consistent investing is not without difficulties, and practices vary across regions. The survey also reveals that faith consistent investing has many commonalities with secular responsible investors.
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A new cross-validation technique to evaluate quality of recommender systems(2012)The topic of recommender systems is rapidly gaining interest in the user-behaviour modeling research domain. Over the years, various recommender algorithms based on different mathematical models have been introduced in the literature. Researchers interested in proposing a new recommender model or modifying an existing algorithm should take into account a variety of key performance indicators, such as execution time, recall and precision. Till date and to the best of our knowledge, no general cross-validation scheme to evaluate the performance of recommender algorithms has been developed. To fill this gap we propose an extension of conventional cross-validation. Besides splitting the initial data into training and test subsets, we also split the attribute description of the dataset into a hidden and visible part. We then discuss how such a splitting scheme can be applied in practice. Empirical validation is performed on traditional user-based and item-based recommender algorithms which were applied to the MovieLens dataset.