Summary of Leveraging World Events to Predict E-commerce Consumer Demand Under Anomaly, by Dan Kalifa et al.
Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly
by Dan Kalifa, Uriel Singer, Ido Guy, Guy D. Rosin, Kira Radinsky
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to consumer demand forecasting for e-commerce applications, specifically during periods with many anomalies such as pandemics, abnormal weather, or sports events. The authors hypothesize that leveraging external knowledge found in world events can help overcome the challenge of prediction under anomalies. They mine a large repository of 40 years of world events and their textual representations using transformers to construct an embedding of a day based on the relations of the day’s events. These embeddings are then used to forecast future consumer behavior. The authors empirically evaluate the methods over a large e-commerce products sales dataset extracted from eBay, one of the world’s largest online marketplaces, showing that their method outperforms state-of-the-art baselines during anomalies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to predict what people will buy on online marketplaces like eBay. It’s hard to make accurate predictions when there are lots of unusual events happening, like a pandemic or a big sports game. The researchers think that by looking at world news and events, they can make better predictions. They collect 40 years’ worth of news articles and use special computer programs to turn the news into numbers that represent each day’s events. These numbers are then used to forecast what people will buy in the future. The results show that their method is better than others at making accurate predictions during unusual times. |
Keywords
» Artificial intelligence » Embedding
