Session 5b: E-commerce & Ads

Date: Friday, Sept 18, 2015, 16:30-18:00
Location: HS 5
Chair: Paolo Cremonesi

• Selection and Ordering of Linear Online Video Ads
  by Wreetabrata Kar, Viswanathan Swaminathan and Paulo Albuquerque

  This paper studies the selection and ordering of in-stream ads in videos shown in online content publishers. We propose an allocation algorithm that uses a collective measure of price and quality for each ad and factors in slot-specific continuation probabilities to maximize publisher revenue. The algorithm is based on cascade models and uses a dynamic programming method to assign linear (video) ads to slots in an online video. The approach accounts for the negative externality created by lower quality ads placed in a video, leading to viewer exit and thereby preventing the publisher from showing the subsequent ads scheduled in that session. Our algorithm is scalable and suited for real-time applications. A large log of viewer activity from a video ad platform is used to empirically test the algorithm. A series of simulations show that our algorithm, when compared to other algorithms currently practiced in industry, generates more revenue for the publisher and increases viewer retention.

• Adaptation and Evaluation of Recommendations for Short-term Shopping Goals
  by Dietmar Jannach, Lukas Lerche and Michael Jugovac

  An essential characteristic in many of e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for recommendation. Simple “real-time” recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user’s long-term preference profile.

  In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long term preferences is particularly important at the beginning of new shopping sessions.

• E-commerce Recommendation with Personalized Promotion
  by Qi Zhao, Yi Zhang, Daniel Friedman and Fangfang Tan

  Most existing e-commerce recommender systems aim to recommend the right products to a consumer, assuming the properties of each product are fixed. However, some properties, including price discount, can be personalized to respond to each consumer’s preference. This paper studies how to automatically set the price discount when recommending a product, in light of the fact that the price often will alter a consumer’s purchase decision. The key to optimizing the discount is to predict consumer’s willingness-to-pay (WTP), namely, the highest price a consumer is willing to pay for a product. Purchase data used by traditional e-commerce recommender systems provide points below or above the decision boundary. In this paper we collected training data to better predict the decision boundary. We implement a new e-commerce mechanism adapted from laboratory lottery and auction experiments that elicit a rational customer’s exact WTP for a small subset of products, and use a machine learning algorithm to predict the customer’s WTP for other products. The mechanism is implemented on our own e-commerce website that leverages Amazon’s data and subjects recruited via Mechanical Turk. The experimental results demonstrate that the proposed approach can better predict WTP, dramatically improve conversion rate and sales revenue.

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