Implicit data collection

Implicit data collection refers to techniques in human–computer interaction and recommender systems that infer user preferences from observed behavior rather than explicit input.

Overview

Implicit data are used to construct a user model from interaction traces such as clicks, purchases, or dwell time. These signals enable information filtering and personalization in recommender systems and search.

In recommender systems, implicit feedback is often modeled through techniques such as matrix factorization and pairwise ranking, which treat user interactions as positive-only or preference signals.

Data sources

Implicit signals include behavioral and contextual data, such as:

interaction logs (clicks, views, purchases)
dwell time and browsing patterns
contextual and device information
multimodal signals (e.g., gaze, voice, or facial expression)

These signals are typically noisy and require modeling assumptions to distinguish preference from exposure.

References

↑ Ricci, Francesco; Rokach, Lior; Shapira, Bracha (2015). Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook. Springer. doi:10.1007/978-1-4899-7637-6. ISBN 978-1-4899-7636-9.
↑ Joachims, Thorsten (2002). "Optimizing search engines using clickthrough data". Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 133–142. doi:10.1145/775047.775067. ISBN 1-58113-567-X.
↑ Hu, Yifan; Koren, Yehuda; Volinsky, Chris (2008). "Collaborative Filtering for Implicit Feedback Datasets". 2008 Eighth IEEE International Conference on Data Mining. pp. 263–272. doi:10.1109/ICDM.2008.22. ISBN 978-0-7695-3502-9.
↑ Rendle, Steffen; Freudenthaler, Christoph; Gantner, Zeno; Schmidt-Thieme, Lars (2012). "BPR: Bayesian Personalized Ranking from Implicit Feedback". Proceedings of the Conference on Uncertainty in Artificial Intelligence. arXiv:1205.2618.
↑ Hu, Yifan; Koren, Yehuda; Volinsky, Chris (2008). "Collaborative Filtering for Implicit Feedback Datasets". Proceedings of the IEEE International Conference on Data Mining. Bibcode:2008icdm.conf...43H.

[1] Ricci, Francesco; Rokach, Lior; Shapira, Bracha (2015). Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook. Springer. doi:10.1007/978-1-4899-7637-6. ISBN 978-1-4899-7636-9.

[2] Joachims, Thorsten (2002). "Optimizing search engines using clickthrough data". Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 133–142. doi:10.1145/775047.775067. ISBN 1-58113-567-X.

[3] Hu, Yifan; Koren, Yehuda; Volinsky, Chris (2008). "Collaborative Filtering for Implicit Feedback Datasets". 2008 Eighth IEEE International Conference on Data Mining. pp. 263–272. doi:10.1109/ICDM.2008.22. ISBN 978-0-7695-3502-9.

[4] Rendle, Steffen; Freudenthaler, Christoph; Gantner, Zeno; Schmidt-Thieme, Lars (2012). "BPR: Bayesian Personalized Ranking from Implicit Feedback". Proceedings of the Conference on Uncertainty in Artificial Intelligence. arXiv:1205.2618.

[5] Hu, Yifan; Koren, Yehuda; Volinsky, Chris (2008). "Collaborative Filtering for Implicit Feedback Datasets". Proceedings of the IEEE International Conference on Data Mining. Bibcode:2008icdm.conf...43H.