See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1145/3511808.3557694
Many recommender systems optimize click through rates (CTRs) as one of their core goals, and it further breaks down to predicting each item's click probability for a user (user-item click probability) and recommending the top ones to this particular user. User-item click probability is then estimated as a single term, and the basic assumption is that the user has different preferences over items. This is presumably true, but from real-world data, we observe that some people are naturally more active in clicking on items while some are not. This intrinsic tendency contributes to their user-item click probabilities. Besides this, when a user sees a particular item she likes, the click probability for this item increases due to this user-item preference.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3511808.3557694
- https://dl.acm.org/doi/pdf/10.1145/3511808.3557694
- OA Status
- hybrid
- Cited By
- 2
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306317229