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As part of our project, we tag learning objects with both objective (e.g., title, date, and author) and subjective (e.g., quality and relevance) metadata. We present the RACOFI (Rule\u2010Applying Collaborative Filtering) Composer prototype with its novel combination of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We developed RACOFI to generate context\u2010aware recommendation lists. Context is handled by multidimensional predictions produced from a database\u2010driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The RACOFI Composer architecture has been developed into the contextaware music portal inDiscover.<\/jats:p>","DOI":"10.1108\/17415650580000043","type":"journal-article","created":{"date-parts":[[2010,6,5]],"date-time":"2010-06-05T07:20:15Z","timestamp":1275722415000},"page":"179-188","source":"Crossref","is-referenced-by-count":37,"title":["Collaborative filtering and inference rules for context\u2010aware learning object recommendation"],"prefix":"10.1108","volume":"2","author":[{"given":"Daniel","family":"Lemire","sequence":"first","affiliation":[]},{"given":"Harold","family":"Boley","sequence":"additional","affiliation":[]},{"given":"Sean","family":"McGrath","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"Ball","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"volume-title":"COLA'03","author":"Anderson M.","key":"p_1"},{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245124"},{"volume-title":"Implementing RuleML Using Schemas, Translators, and Bidirectional Interpreters. 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