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We propose a collaborative filtering (CF) model to predict user satisfaction in SDS evaluation. Inspired by the use of CF in recommendation systems, where a user's preference for a new item is assume to resemble that for similar items rated previously, we adapt the idea to predict user evaluations of unrated dialogs based on the ratings received by similar dialogs. Ratings of dialogs are gathered...
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