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The Web is the largest public big data repository that humankind has created. In this overwhelming data ocean we need to be aware of the quality and in particular, of biases that exist in this data, such as redundancy, spam, etc. These biases affect the machine learning algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, specially in the context of recommendation systems. We give several examples and their relation to sparsity, novelty, and privacy, stressing the importance of the user context to avoid these biases.