Itinerary-aware Personalized Deep Matching at Fliggy Article Swipe
Jia Xu
,
Ziyi Wang
,
Zulong Chen
,
Detao Lv
,
Yao Yu
,
Chuanfei Xu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3442381.3449803
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3442381.3449803
Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users' profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user's itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user's travel intention or preference, and handling the diversity in a user's need. Further, two training objectives, i.e., prediction accuracy of user's travel intention and prediction accuracy of user's click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users' travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3442381.3449803
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- gold
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All OpenAlex metadata
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https://openalex.org/W3155477400Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3442381.3449803Digital Object Identifier
- Title
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Itinerary-aware Personalized Deep Matching at FliggyWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-04-19Full publication date if available
- Authors
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Jia Xu, Ziyi Wang, Zulong Chen, Detao Lv, Yao Yu, Chuanfei XuList of authors in order
- Landing page
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https://doi.org/10.1145/3442381.3449803Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1145/3442381.3449803Direct OA link when available
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Computer science, Matching (statistics), Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2024: 1, 2023: 2, 2022: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(FitNET) | 59 |
| abstract_inverted_index.Further, | 195 |
| abstract_inverted_index.Matching | 0 |
| abstract_inverted_index.accuracy | 201, 208 |
| abstract_inverted_index.business | 22 |
| abstract_inverted_index.compared | 269 |
| abstract_inverted_index.deployed | 278 |
| abstract_inverted_index.designed | 67 |
| abstract_inverted_index.employed | 79 |
| abstract_inverted_index.handling | 188 |
| abstract_inverted_index.implicit | 130 |
| abstract_inverted_index.matching | 57, 73 |
| abstract_inverted_index.methods. | 272 |
| abstract_inverted_index.network, | 74 |
| abstract_inverted_index.problem, | 145 |
| abstract_inverted_index.profiles | 147 |
| abstract_inverted_index.proposed | 94 |
| abstract_inverted_index.sparsity | 144 |
| abstract_inverted_index.superior | 267 |
| abstract_inverted_index.systems, | 84 |
| abstract_inverted_index.traffic. | 284 |
| abstract_inverted_index.training | 197 |
| abstract_inverted_index.utilized | 214 |
| abstract_inverted_index.alleviate | 142 |
| abstract_inverted_index.attention | 157 |
| abstract_inverted_index.behavior, | 212 |
| abstract_inverted_index.carefully | 172 |
| abstract_inverted_index.designed. | 173 |
| abstract_inverted_index.diversity | 190 |
| abstract_inverted_index.important | 17 |
| abstract_inverted_index.inferred. | 140 |
| abstract_inverted_index.inferring | 180 |
| abstract_inverted_index.intention | 132, 184, 205 |
| abstract_inverted_index.itinerary | 91, 120, 166 |
| abstract_inverted_index.optimized | 224 |
| abstract_inverted_index.platforms | 32 |
| abstract_inverted_index.sparsity, | 43 |
| abstract_inverted_index.Meanwhile, | 152 |
| abstract_inverted_index.accurately | 139 |
| abstract_inverted_index.categories | 170 |
| abstract_inverted_index.challenges | 40 |
| abstract_inverted_index.diversity, | 44 |
| abstract_inverted_index.experiment | 228 |
| abstract_inverted_index.increasing | 20 |
| abstract_inverted_index.industrial | 82 |
| abstract_inverted_index.mechanisms | 158, 175 |
| abstract_inverted_index.objectives | 221 |
| abstract_inverted_index.prediction | 200, 207 |
| abstract_inverted_index.production | 231 |
| abstract_inverted_index.technology | 18 |
| abstract_inverted_index.unconsumed | 110 |
| abstract_inverted_index.cardinality | 12 |
| abstract_inverted_index.challenges. | 64 |
| abstract_inverted_index.effectively | 252 |
| abstract_inverted_index.intentions, | 256 |
| abstract_inverted_index.itineraries | 264 |
| abstract_inverted_index.objectives, | 198 |
| abstract_inverted_index.performance | 268 |
| abstract_inverted_index.preference, | 186 |
| abstract_inverted_index.incorporated | 149 |
| abstract_inverted_index.interactions | 163 |
| abstract_inverted_index.preferences, | 257 |
| abstract_inverted_index.successfully | 78, 277 |
| abstract_inverted_index.implicitness. | 46 |
| abstract_inverted_index.effectiveness. | 88 |
| abstract_inverted_index.recommendation | 83, 99 |
| abstract_inverted_index.ITinerary-aware | 55 |
| abstract_inverted_index.itinerary-aware | 156 |
| abstract_inverted_index.simultaneously. | 225 |
| abstract_inverted_index.state-of-the-art | 271 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.8828741 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |