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Revenue Maximization of Airbnb Marketplace using Search Results [article]

Jiawei Wen, Hossein Vahabi, Mihajlo Grbovic
2019 arXiv   pre-print
Correctly pricing products or services in an online marketplace presents a challenging problem and one of the critical factors for the success of the business.  ...  Offline evaluation results show that our strategy improves upon baseline pricing strategies on key metrics by at least +20% in terms of booking regret and +55% in terms of revenue potential.  ...  On the other hand, the marketplace also leverages the signals about the inferred product value when surfacing products to buyers who are searching, i.e. in product recommendation and search ranking algorithms  ... 
arXiv:1911.05887v2 fatcat:sxpifcz2bvg6pakgdwftmdvei4

Movie recommender system for profit maximization

Amos Azaria, Avinatan Hassidim, Sarit Kraus, Adi Eshkol, Ofer Weintraub, Irit Netanely
2013 Proceedings of the 7th ACM conference on Recommender systems - RecSys '13  
(e.g. revenue), without any significant drop in user satisfaction.  ...  Differences in user satisfaction between the lists is negligible, and not statistically significant.  ...  The Revenue Maximizing setting: In this case the goal of the recommender system is to maximize the expected revenue, e.g. by recommending expensive items.  ... 
doi:10.1145/2507157.2507162 dblp:conf/recsys/AzariaHKEWN13 fatcat:3yxhlndegrey5dfufejntdvdie

Learning Theory and Algorithms for Revenue Management in Sponsored Search [article]

Lulu Wang, Huahui Liu, Guanhao Chen, Shaola Ren, Xiaonan Meng, Yi Hu
2018 arXiv   pre-print
And then, we design an explicit ranking function by incorporating the calibration fac-tor and price-squashed factor to maximize the revenue.  ...  In particular, our proposed methods perform better than the state-of-the-art methods with regard to the revenue of the platform.  ...  We also explored the ranking functions, both implicit and explicit ones, to maximize the revenue in sponsored search. The methods are deployed on two production platforms.  ... 
arXiv:1807.01827v1 fatcat:j43yigb5crcmjbrjfvk5vjoojm

NMA: Neural Multi-slot Auctions with Externalities for Online Advertising [article]

Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu, Yongkang Wang, Xingxing Wang, Dong Wang
2022 arXiv   pre-print
We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning.  ...  Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms.  ...  In GSP, ads are ranked by the product of click bid and predicted CTR.  ... 
arXiv:2205.10018v1 fatcat:hykd66pe35gylphtnfxa6pz4ai

A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders [article]

Phong Nguyen, John Dines, Jan Krasnodebski
2017 arXiv   pre-print
correlation metric and its kernel version; given an initial ranking of item recommendations built for the consumer, we aim to re-rank it such that the new ranking is also optimized for the secondary objectives  ...  In addition to the consumer stakeholder, we also consider two other stakeholders; the suppliers who provide the goods and services for sale and the intermediary who is responsible for helping connect consumers  ...  Lastly, we should note the work on revenue optimization in sponsored search, in which the dual problem of placing ads relevant to the customer while maximizing revenue of the platform is approached [11  ... 
arXiv:1708.00651v2 fatcat:x237aiagnvb25kc55eb6ez4jsq

Price Optimization in Fashion E-commerce [article]

Sajan Kedia, Samyak Jain, Abhishek Sharma
2020 arXiv   pre-print
By establishing an optimal price point, they can maximize overall revenue and profit for the platform.  ...  In this paper, we propose a novel machine learning and optimization technique to find the optimal price point at an individual product level. It comprises three major components.  ...  Now we need to choose one of these three prices such that the net revenue is maximized. Given 3 different price points and N products, there will be 3 N permutations in total.  ... 
arXiv:2007.05216v2 fatcat:5hjr42d225ajpl6ldgfee4snr4

A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search [article]

Di He, Wei Chen, Liwei Wang, Tie-Yan Liu
2014 arXiv   pre-print
Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences.  ...  In this paper, we propose a novel game-theoretic machine learning approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework  ...  Acknowledgments This work is supported by NSFC(61222307, 61075003) and NCET-12-0015, and the work was done when the first author was visiting Microsoft Research Asia.  ... 
arXiv:1406.0728v2 fatcat:lol4vrvptrhftn7f3ck4twhldq

Learning to Rank and Discover for E-Commerce Search [chapter]

Anjan Goswami, Chengxiang Zhai, Prasant Mohapatra
2018 Lecture Notes in Computer Science  
To this end, we develop a formal framework for optimizing E-Com search and propose a novel epsilon-explore Learning to Rank (eLTR) paradigm that can be integrated with the traditional learning to rank  ...  relevance and revenue because of that.  ...  However, in e-com search we also need to ensure maximization of revenue and the exploration needs to be well regulated to minimize the expected loss. Vermorel et al.  ... 
doi:10.1007/978-3-319-96133-0_25 fatcat:5vekxxsuzjdarith4ufv2c52ni

An Adaptive Online Ad Auction Scoring Algorithm for Revenue Maximization [article]

Chenyang Li, Mingyi Hong, Randy Cogill, Alfredo Garcia
2012 arXiv   pre-print
Advertisers express their willingness to pay for each keyword in terms of bids to the search engine.  ...  The advertiser only pays the search engine when its ad is clicked by the user and the price-per-click is determined by the bids of other competing advertisers.  ...  We also obtain the final revenue for the search engine to be Πse(σ) = 122, 032.85, which is 99.07% of the social optimal  ... 
arXiv:1207.4701v1 fatcat:d6zib2jscfgs7gaqea744owva4

Response Transformation and Profit Decomposition for Revenue Uplift Modeling

Robin M. Gubela, Stefan Lessmann, Szymon Jaroszewicz
2019 European Journal of Operational Research  
If customers differ in their spending behavior, revenue maximization is a more plausible business objective compared to maximizing conversions.  ...  Remedies to this modeling challenge are incorporated in the proposed revenue uplift strategies in the form of two-stage models.  ...  Finally, we very much appreciate the time and efforts of the editor, Prof. Robert Graham Dyson in handling our paper.  ... 
doi:10.1016/j.ejor.2019.11.030 fatcat:nl6e3r3cbfewdmqub7dvpxe5i4

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising [article]

Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen (+1 others)
2021 arXiv   pre-print
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue.  ...  Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare).  ...  , 62072303, 61902248, and 61972254, and in part by Alibaba Group through Alibaba Innovation Research Program, and in part by Shanghai Science and Technology fund 20PJ1407900.  ... 
arXiv:2106.03593v2 fatcat:7o6z4bq2gbh3fcan2nusgcnmae

U-rank

Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue.  ...  Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.  ...  In the experiment with bid in Scenario 2, the improvements are 2.5%, 13%, 6.7% and 3.9% in terms of Revenue, Revenue@1, Revenue@3 and Revenue@5, respectively.  ... 
doi:10.1145/3340531.3412756 dblp:conf/cikm/DaiHLXT0H0020 fatcat:ejnob5uzdfakro6zl4xlmzzpuu

Ranking an Assortment of Products via Sequential Submodular Optimization [article]

Arash Asadpour, Rad Niazadeh, Amin Saberi, Ali Shameli
2020 arXiv   pre-print
products in response to a search query?  ...  In order to capture both popularity and diversity effects, we model the probability that a user clicks on an element from a subset of products as a monotone submodular function of this set.  ...  in Section 3, Theorem 1.  ... 
arXiv:2002.09458v1 fatcat:7vr6agjxenbrvora5fzcqtxw6i

A hybrid TOPSIS-BSC method for strategic planning

Mohammad Reza Shojaee, Mehdi Fallah, Mohsen Fallah
2012 Management Science Letters  
In this paper, we present a study to setup appropriate strategies using the implementation of balanced score card in four perspectives of customers, processes, learning and financial.  ...  Therefore, the study uses BSC for the first two important strategies and discusses possible actions for productivity improvement.  ...  Acknowledgment The authors would like to thank the experts who cordially participated in our survey and provided some actions for productivity improvement.  ... 
doi:10.5267/j.msl.2012.09.029 fatcat:xfpoukyn7jg2dmfe4o66bein7u

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising [article]

Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai
2021 arXiv   pre-print
In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework.  ...  In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.  ...  ACKNOWLEDGMENTS This work was supported in part by Science and Technology In-  ... 
arXiv:2012.02930v2 fatcat:s3jng4wt3nf27fpqkhnqemwclm
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