A Practical Exploration System for Search Advertising

Shah, Parikshit; Yang, Ming; Alle, Sachidanand; Ratnaparkhi, Adwait; Shahshahani, Ben; Chandra, Rohit

doi:10.1145/3097983.3098041

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…To the best of our knowledge we are the first to demonstrate active DP algorithms in a real e-commerce production system. Furthermore, while typical applications of MAB algorithms in production systems have dealt with the case when there are only finite number of arms Agarwal et al (2016); Shah et al (2017), our paper is the first to demonstrate practical application of a more involved bandit optimization problem which has infinite arms.…”

Section: Introductionmentioning

confidence: 91%

Thompson Sampling for Dynamic Pricing

Ganti,

Sustik,

Tran

et al. 2018

Preprint

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In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.

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Section: Introductionmentioning

confidence: 91%

Thompson Sampling for Dynamic Pricing

Ganti,

Sustik,

Tran

et al. 2018

Preprint

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“…Yi et al, 2016 implemented a movie cold-start recommendation method to optimize the movie similarity measure by computing the similarities among directors and actors using Item-based collaborative filtering [12]. Shah et al, 2017 in their work designed a new exploration system that was adapted to search advertising [8]. In this paper, an -greedy exploration algorithm (that takes search term and advertiser bid into account) is used to deal with the new item/advertisement problem.…”

Section: New Itemmentioning

confidence: 99%

“…The new item-When a new item is inserted into the system (e.g., a new movie) [12,13]. Because this item is not associated with any users yet, personalization models face difficulties when recommending it [14,15].…”

mentioning

confidence: 99%

A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

Viktoratos

Tsadiras

2022

Algorithms

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A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A problem occurs when the user has limited or no data available and, therefore, the algorithms cannot work well. This situation is widely referred in the literature as the ‘cold-start’ case. The aim of this manuscript is to explore this problem and present a prediction approach for personalized mobile advertising systems that addresses the cold-start, and especially the frozen user case, when a user has no data at all. The approach consists of three steps: (a) identify existing datasets and use specific attributes that could be gathered from a frozen user, (b) train and test machine learning models in the existing datasets and predict click-through rate, and (c) the development phase and the usage in a system.

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“…Another possible way to tackle this problem is to actively collect more training data in a short time. For example, [20,27,44,46] use contextual-bandit approaches and [10,12,34,58] design interviews to collect specific information with active learning. However, these approaches still cannot lead to satisfactory prediction performance before sufficient training data are collected.…”

Section: Introductionmentioning

confidence: 99%

Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

Ouyang

Zhang

Ren

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order data nonlinearity have shown dramatic successes in CTR prediction. However, these models work poorly on cold-start ads with new IDs, whose embeddings are not well learned yet. In this paper, we propose Graph Meta Embedding (GME) models that can rapidly learn how to generate desirable initial embeddings for new ad IDs based on graph neural networks and meta learning. Previous works address this problem from the new ad itself, but ignore possibly useful information contained in existing old ads. In contrast, GMEs simultaneously consider two information sources: the new ad and existing old ads. For the new ad, GMEs exploit its associated attributes. For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information. We propose three specific GMEs from different perspectives to explore what kind of information to use and how to distill information. In particular, GME-P uses Pre-trained neighbor ID embeddings, GME-G uses Generated neighbor ID embeddings and GME-A uses neighbor Attributes. Experimental results on three real-world datasets show that GMEs can significantly improve the prediction performance in both cold-start (i.e., no training data is available) and warm-up (i.e., a small number of training samples are collected) scenarios over five major deep learning-based CTR prediction models. GMEs can be applied to conversion rate (CVR) prediction as well.

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A Practical Exploration System for Search Advertising

Cited by 10 publications

References 6 publications

Thompson Sampling for Dynamic Pricing

Thompson Sampling for Dynamic Pricing

A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

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