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Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively smalldoi:10.1145/3269206.3269268 dblp:conf/cikm/PandeyKS18 fatcat:brhmmkne3jcgvcfs5snclolvxm