Implicit data collection
| Recommender systems |
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| Concepts |
| Methods and challenges |
| Implementations |
| Research |
Implicit data collection refers to techniques in human–computer interaction and recommender systems that infer user preferences from observed behavior rather than explicit input.[1]
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.[2]
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.[3][4]
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.[5]
References
- ↑ Ricci, Francesco; Rokach, Lior; Shapira, Bracha (2015). 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.
- ↑ 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.
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