We propose a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between items using techniques drawn from information retrieval. We show how this representation can be used to explain the similarities between items using terms from the textual aura and further how it can be used to steer the recommender. We describe a system that demonstrates these techniques and we'll detail some preliminary experiments aimed at evaluating the quality of the recommendations and the effectiveness of the explanations of item similarity.

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as "chill" or "jogging" that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or "autotags") furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ''cold-start problem'' common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system."

Most current automatic hypertext generation systems rely on term repetition to calculate the relatedness of two documents. There are well-recognized problems with such approaches, most notably, a vulnerability to the effects of synonymy (many words for the same concept) and polysemy (many concepts for the same word). We propose a novel method for automatic hypertext generation that is based on a technique called lexical chaining, a method for discovering sequences of related words in a text. This method uses a more general notion of document relatedness, and attempts to take into account the effects of synonymy and polysemy. We also present the results of an empirical study designed to test this method in the context of a question answering task from a database of newspaper articles.

this paper will be incorporated into into the Nigel grammar of the Penman system to provide a fine degree of stylistic control in language generation