See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction Article Swipe
Shiwei Lyu
,
Hongbo Cai
,
Chaohe Zhang
,
Shuai Ling
,
Yue Shen
,
Xiaodong Zeng
,
Jinjie Gu
,
Guannan Zhang
,
Haipeng Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3511808.3557694
YOU?
·
· 2022
· Open Access
·
· 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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Metadata
- 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
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306317229Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3511808.3557694Digital Object Identifier
- Title
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See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR PredictionWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-16Full publication date if available
- Authors
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Shiwei Lyu, Hongbo Cai, Chaohe Zhang, Shuai Ling, Yue Shen, Xiaodong Zeng, Jinjie Gu, Guannan Zhang, Haipeng ZhangList of authors in order
- Landing page
-
https://doi.org/10.1145/3511808.3557694Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3511808.3557694Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3511808.3557694Direct OA link when available
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Computer science, Click-through rate, Recommender system, Preference, Human–computer interaction, Information retrieval, Core (optical fiber), Artificial intelligence, Statistics, Mathematics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.item | 105, 113 |
| abstract_inverted_index.more | 78 |
| abstract_inverted_index.not. | 87 |
| abstract_inverted_index.ones | 35 |
| abstract_inverted_index.over | 61 |
| abstract_inverted_index.sees | 102 |
| abstract_inverted_index.some | 74, 85 |
| abstract_inverted_index.that | 55, 73 |
| abstract_inverted_index.then | 44 |
| abstract_inverted_index.this | 37, 112, 117 |
| abstract_inverted_index.user | 27, 57, 101 |
| abstract_inverted_index.when | 99 |
| abstract_inverted_index.basic | 52 |
| abstract_inverted_index.click | 4, 23, 29, 41, 95, 109 |
| abstract_inverted_index.data, | 70 |
| abstract_inverted_index.items | 83 |
| abstract_inverted_index.rates | 6 |
| abstract_inverted_index.term, | 49 |
| abstract_inverted_index.their | 11, 93 |
| abstract_inverted_index.this, | 98 |
| abstract_inverted_index.true, | 66 |
| abstract_inverted_index.user. | 39 |
| abstract_inverted_index.while | 84 |
| abstract_inverted_index.(CTRs) | 7 |
| abstract_inverted_index.active | 79 |
| abstract_inverted_index.breaks | 17 |
| abstract_inverted_index.goals, | 13 |
| abstract_inverted_index.item's | 22 |
| abstract_inverted_index.items. | 62 |
| abstract_inverted_index.likes, | 107 |
| abstract_inverted_index.people | 75 |
| abstract_inverted_index.single | 48 |
| abstract_inverted_index.Besides | 97 |
| abstract_inverted_index.further | 16 |
| abstract_inverted_index.observe | 72 |
| abstract_inverted_index.systems | 2 |
| abstract_inverted_index.through | 5 |
| abstract_inverted_index.clicking | 81 |
| abstract_inverted_index.optimize | 3 |
| abstract_inverted_index.tendency | 90 |
| abstract_inverted_index.User-item | 40 |
| abstract_inverted_index.different | 59 |
| abstract_inverted_index.estimated | 45 |
| abstract_inverted_index.increases | 114 |
| abstract_inverted_index.intrinsic | 89 |
| abstract_inverted_index.naturally | 77 |
| abstract_inverted_index.user-item | 94, 118 |
| abstract_inverted_index.(user-item | 28 |
| abstract_inverted_index.assumption | 53 |
| abstract_inverted_index.particular | 38, 104 |
| abstract_inverted_index.predicting | 20 |
| abstract_inverted_index.presumably | 65 |
| abstract_inverted_index.real-world | 69 |
| abstract_inverted_index.contributes | 91 |
| abstract_inverted_index.preference. | 119 |
| abstract_inverted_index.preferences | 60 |
| abstract_inverted_index.probability | 24, 42, 110 |
| abstract_inverted_index.recommender | 1 |
| abstract_inverted_index.probability) | 30 |
| abstract_inverted_index.recommending | 32 |
| abstract_inverted_index.probabilities. | 96 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.53958817 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |