ABSTRACT
User engagement evaluation task in social networks has recently attracted considerable attention due to its applications in recommender systems. In this task, the posts containing users' opinions about items, e.g., the tweets containing the users' ratings about movies in the IMDb website, are studied. In this paper, we try to make use of tweets from different web applications to improve the user engagement evaluation performance. To this aim, we propose an adaptive method based on multi-task learning. Since in this paper we study the problem of detecting tweets with positive engagement which is a highly imbalanced classification problem, we modify the loss function of multi-task learning algorithms to cope with the imbalanced data. Our evaluations over a dataset including the tweets of four diverse and popular data sources, i.e., IMDb, YouTube, Goodreads, and Pandora, demonstrate the effectiveness of the proposed method. Our findings suggest that transferring knowledge between data sources can improve the user engagement evaluation performance.
- R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. In ECML, pages 39--50, 2004.Google ScholarDigital Library
- J. G. C. de Souza, H. Zamani, M. Negri, M. Turchi, and D. Falavigna. Multitask learning for adaptive quality estimation of automatically transcribed utterances. In NAACL-HLT, pages 714--724, 2015.Google ScholarCross Ref
- R. Caruana. Multitask learning. Machine Learning, 28(1):41--75, 1997. Google ScholarDigital Library
- J. Chen, J. Liu, and J. Ye. Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Trans. Knowl. Discov. Data, 5(4):22:1--22:31, 2012. Google ScholarDigital Library
- J. Chen, L. Tang, J. Liu, and J. Ye. A convex formulation for learning shared structures from multiple tasks. In ICML, pages 137--144, 2009. Google ScholarDigital Library
- S. Dooms, T. De Pessemier, and L. Martens. Mining cross-domain rating datasets from structured data on twitter. In MSM@WWW, 2014. Google ScholarDigital Library
- D. Loiacono, A. Lommatzsch, and R. Turrin. An analysis of the 2014 recsys challenge. In RecSys Challenge, pages 1--6, 2014. Google ScholarDigital Library
- S. Petrovic, M. Osborne, and V. Lavrenko. Rt to win! predicting message propagation in twitter. In ICWSM, pages 586--589, 2011.Google Scholar
- D. Powers. Evaluation: From precision, recall and f-measure to roc, informedness, markedness & correlation. J. Mach. Learn. Tech., 2(1):37--63, 2011.Google ScholarCross Ref
- I. Uysal and W. B. Croft. User oriented tweet ranking: A filtering approach to microblogs. In CIKM, pages 2261--2264, 2011. Google ScholarDigital Library
- H. Zamani, A. Shakery, and P. Moradi. Regression and learning to rank aggregation for user engagement evaluation. In RecSys Challenge, pages 29--34, 2014. Google ScholarDigital Library
Index Terms
Adaptive User Engagement Evaluation via Multi-task Learning
Recommendations
Predicting User Engagement in Twitter with Collaborative Ranking
RecSysChallenge '14: Proceedings of the 2014 Recommender Systems ChallengeCollaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-...
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalExplaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for ...
A brief review on multi-task learning
Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical ...
Comments