Recommending Serendipitous Items using Transfer Learning

Gaurav Pandey, Denis Kotkov, Alexander Semenov
2018 Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18  
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 small
more » ... r of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items. SerRec uses transfer learning to firstly train a deep neural network for relevance scores using a large dataset and then tunes it for serendipity scores using a smaller dataset. Our method shows benefits of transfer learning for recommending serendipitous items as well as performance gains over the state-of-the-art serendipity oriented algorithms.
doi:10.1145/3269206.3269268 dblp:conf/cikm/PandeyKS18 fatcat:brhmmkne3jcgvcfs5snclolvxm