Managing Diversity in Airbnb Search

Abdool, Mustafa; Haldar, Malay; Ramanathan, P.; Sax, Tyler; Zhang, Lanbo; Manaswala, Aamir; Yang, Lynn; Turnbull, Bradley; Zhang, Qing; Legrand, Thomas

doi:10.1145/3394486.3403345

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…DESA [Qin et al, 2020] explores to leverage item dependencies in terms of both relevance and diversity, which is composed of an encoder and a decoder with the self-attention to extract item and subtopic correlations. Abdool et al [2020] investigate the potential of deploying a diversity-aware re-ranking to Airbnb search. They design a metric for measuring the distance between two lists and use an LSTM structure to generate the re-ranking list.…”

Section: Diversity-aware Re-rankingmentioning

confidence: 99%

Neural Re-ranking in Multi-stage Recommender Systems: A Review

Liu¹,

Yunjia²,

Qin³

et al. 2022

Preprint

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As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects users' experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide benchmarks of the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available 1 .

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Section: Diversity-aware Re-rankingmentioning

confidence: 99%

Neural Re-ranking in Multi-stage Recommender Systems: A Review

Liu¹,

Yunjia²,

Qin³

et al. 2022

Preprint

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“…Aggregation considers the diversity across all users, where the goal is usually to promote the coverage of the recommender [14,15]. On the other hand, individual, also the one we study in this work, focuses on the diversity for a given user, where we usually want to achieve the best trade-off between quality and diversity [1,2,8,37]. Under the individual perspective, diversified recommendation is usually formulated by an optimization problem with an objective considering both quality and diversity.…”

Section: Related Work 21 Diversified Recommendationmentioning

confidence: 99%

Sliding Spectrum Decomposition for Diversified Recommendation

Huang

Wang

Zhang

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Content feed, a type of product that recommends a sequence of items for users to browse and engage with, has gained tremendous popularity among social media platforms. In this paper, we propose to study the diversity problem in such a scenario from an item sequence perspective using time series analysis techniques. We derive a method called sliding spectrum decomposition (SSD) that captures users' perception of diversity in browsing a long item sequence. We also share our experiences in designing and implementing a suitable item embedding method for accurate similarity measurement under long tail effect. Combined together, they are now fully implemented and deployed in Xiaohongshu App's production recommender system that serves the main Explore Feed product for tens of millions of users every day. We demonstrate the effectiveness and efficiency of the method through theoretical analysis, offline experiments and online A/B tests.

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“…The authors showed good results on the MovieLens database, doubling the long-tail coverage and being within 1% of NDCG@10 of other existing approaches. Though not aimed at position bias, Airbnb developed a novel deep learning approach that leverages Recurrent Neural Networks for diversification (Abdool et al 2020).…”

Section: Recommendation Systems For Online Travelmentioning

confidence: 99%