Those were the days: learning to rank social media posts for reminiscence

Naini K. D. , Kawase R., Kanhabua N., Niederee C., ALTINGÖVDE İ. S.

INFORMATION RETRIEVAL JOURNAL, vol.22, pp.159-187, 2019 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22
  • Publication Date: 2019
  • Doi Number: 10.1007/s10791-018-9339-9
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.159-187
  • Keywords: Learning to rank, Letor, Social media, Personalization, Personalized ranking, Content retention, Social features, Feature selection, Facebook, INFORMATION, MEMORY


Social media posts are a great source for life summaries aggregating activities, events, interactions and thoughts of the last months or years. They can be used for personal reminiscence as well as for keeping track with developments in the lives of not-so-close friends. One of the core challenges of automatically creating such summaries is to decide which posts are memorable, i.e., should be considered for retention and which ones to forget. To address this challenge, we design and conduct user evaluation studies and construct a corpus that captures human expectations towards content retention. We analyze this corpus to identify a small set of seed features that are most likely to characterize memorable posts. Next, we compile a broader set of features that are leveraged to build general and personalized machine-learning models to rank posts for retention. By applying feature selection, we identify a compact yet effective subset of these features. The models trained with the presented feature sets outperform the baseline models exploiting an intuitive set of temporal and social features.