Technical Presentations Group 2: AI for Good + Business Impact/Industry Sales forecasting and product recommendation are important tasks for Business-to-Business (B2B) companies, particularly as more business transactions are occurring through digital channels (eCommerce). Transaction data contains both explicit signals (price, revenue, ratings) and implicit signals (product purchases, user clicks). Sales prediction, based on explicit signals, and product recommendation, based on implicit signals, are commonly achieved with separate machine learning models. We propose a new multi-task learning model framework, which performs a joint optimization to do prediction and recommendation tasks simultaneously. This multi-task deep learning model captures and predicts seasonality in the data and has an effective sampling mechanism to improve implicit feedback for the recommendation task. Our experiments on real B2B transaction datasets have shown that the multi-task model can achieve comparable performance for both tasks compared to single-task models (around 40% lower mean absolute percentage error and 30% improvement in Diversity@K, which is the percentage of overall items that are captured in the top K recommendations). In addition, the multi-task model enables better solutions to problems such as cold start and collaborative filtering
Authors: Wenshen Song (PROS Inc.), Yan Xu (PROS Inc.), Faruk Sengul (PROS Inc.), and Justin Silver (PROS Inc.)
