相关论文: Do Price Ranges Increase Click-throughs?
After years of speculation, price discrimination in e-commerce driven by the personal information that users leave (involuntarily) online, has started attracting the attention of privacy researchers, regulators, and the press. In our…
The finding of small price changes in many retail price datasets is often viewed as a puzzle. We show that a possible explanation for the presence of small price changes is related to sales volume, an observation that has been overlooked in…
The plethora of comparison shopping agents (CSAs) in today's markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed "selective price…
Extensive research shows that consumers are generally averse to price discrimination. However, instruments of differential pricing can benefit consumer surplus and alleviate inequity through targeted price discounts. This paper examines how…
We modify the standard model of price competition with horizontally differentiated products, imperfect information, and search frictions by allowing consumers to flexibly acquire information about a product's match value during their…
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in…
Online stores can present a different price to each customer. Such algorithmic personalised pricing can lead to advanced forms of price discrimination based on the characteristics and behaviour of individual consumers. We conducted two…
It is standard practice in online retail to run pricing experiments by randomizing at the article-level, i.e. by changing prices of different products to identify treatment effects. Due to customers' cross-price substitution behavior, such…
Ranking product recommendations to optimize for a high click-through rate (CTR) or for high conversion, such as add-to-cart rate (ACR) and Order-Submit-Rate (OSR, view-to-purchase conversion) are standard practices in e-commerce. Optimizing…
An online seller or platform is technically able to offer every consumer a different price for the same product, based on information it has about the customers. Such online price discrimination exacerbates concerns regarding the fairness…
Much like small ripples in a stream, which get lost in the larger waves, small changes in retail prices often fly under the radar of public perceptions, while large price changes appear as marketing moves associated with demand and…
This paper studies the widespread price dispersion of homogeneous products across different online platforms, even when consumers can easily access price information from comparison websites. We collect data for the 200 most popular hotels…
To identify the most appropriate recommendation model for an e-commerce business, a live evaluation should be performed on the shopping website to measure the influence of personalization in real-time. The aim of this paper is to introduce…
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research and industry since…
Over the past years, e-commerce platforms have expanded substantially, providing customers with convenient shopping experiences. To enhance e-commerce websites, it's essential to grasp user engagement and factors affecting conversion rates.…
There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or `green' benefits. Some consumers might value these benefits while…
User journeys in e-commerce routinely violate the one-to-one assumption that a clicked item on an advertising platform is the same item later purchased on the merchant's website/app. For a significant number of converting sessions on our…
Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item…
When online sellers use AI learning algorithms to automatically compete on e-commerce platforms, there is concern that they will learn to coordinate on higher than competitive prices. However, this concern was primarily raised in…
Finding a product online can be a challenging task for users. Faceted search interfaces, often in combination with recommenders, can support users in finding a product that fits their preferences. However, those preferences are not always…