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In order to measure the quality of the results of a RS, there is a wide range of metrics which are used to evalúate both the prediction and recommendation quality of these systems [17,1,18,6].CF based RS estímate the valué of an item not voted by a user via the ratings made on that item by a set of similar users. The overall quality in the prediction is called accuracy [3] and the mean absolute error (MAE) is normally used to obtain it [17]. The system's ability to make estimations is called coverage and it indicates the percentage of prediction which we can make using the set of similar users selected (usually, the more similar users we select and the more votes the selected users have cast, the better the coverage we achieve). In RS, besides aiming to improve the quality measures of the predictions (accuracy and coverage), there are other issues that need be taken into account [56,41,51]: avoiding overspecialization phenomena, finding good Ítems, credibility of recommendations, precisión and recall measures, etc.The rest of the paper is divided into the following sections (with the same numbering shown here):2. State of the art, in which a review is made of the most relevant contributions that exist in the CF aspects covered in the paper: cold-start and application of neural networks to the RS. 3. General hypothesis and motivations: what we aim to contribute and the indications that lead us to believe that carrying out research into this subject will provide satisfactory results that support the hypothesis set out. 4. Design of the user cold-start similarity measure: explanation and formalization of the design of the similarity measure proposed as a linear combination of simple similarity measures, by adjusting the weights using optimization techniques based on neural networks. 5. Collaborative filtering specifications: formalization of the CF methodology which specifies the way to predict and recommend, as well as to obtain the quality valúes of the predictions and recommendations. This is the formalization that supports the design of experiments carried out in the paper. The methodology is provided which describes the use of leave-one-out cross validation applied to obtaining the MAE, coverage, precisión and recall. 6. Design of the experiments with which the quality results are obtained provided by the user cold-star similarity measure proposed and by a set of current similarity measures for which we aim to improve the results. We use the Netflix (http:// www.netflixprize.com) and Movielens (http://www.movielens.org) databases. 7. Graphical results obtained in the experiments, complemented with explanations of the behavior of each quality measure. 8. Most relevant conclusions obtained. State of the art The cold-start issueThe cold-start problem [48,1] occurs when it is not possible to make reliable recommendations due to an initial lack of ratings. We can distinguish three kinds of cold-start problems: new community, new item and new user. The last kind is the most important in RS that are already in operat...

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