Personalised recommender systems aim to help users retrieve relevant information from large collections, by automatically finding and suggesting products of likely interest based on observed evidence of the users ’ preferences. These preferences are difficult to guess, and therefore recommender systems have a considerable variance in their success ratio in estimating the user’s tastes and interests. In such a scenario, self-predicting the chances that a recommendation may be accurate before actually submitting it to a user becomes an interesting capability from many perspectives. Performance prediction has been studied in the context of Information Retrieval , but there is little if any prior research of this problem in the recommendation domain. In our work we investigate the definition and formalisation of performance prediction methods for recommender systems . Specifically, we propose and analyse an array of methods based on adaptations of performance predictors from Information Retrieval- mainly the query clarity predictor, which captures the query ambiguity with respect to a given document collection . We define several language models according to various probability spaces (items, ratings, and combinations thereof) to capture different aspects of the users and items involved in the recommendation task. Through a series of empirical experiments on several datasets, we have found strong correlations between the proposed predictors and the performance measured, confirming the predictive power of our methods. Furthermore, we have also found a correspondence between such predictive power and performance enhancements in two applications for recommendation: dynamic recommender ensembles and neighbour selection and weighting. BODY We develop new recommendation effectiveness prediction methods based on query clarity. Recommendation performance is, in fact, predictable
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