Multi-Scenario Ranking with Adaptive Feature Learning

Abstract

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.To this end, in this paper, we propose a multi-scenario ranking framework with adaptive feature learning (named Maria). Specifically, Maria is devised to inject the scenario semantics in the bottom part of the network to derive more discriminative feature representations. There are three components designed in Maria * Corresponding author. Work done when Yu Tian was an intern at Alibaba.

ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation

“…Therefore, exploring how cross-domain transfer learning can utilize the abundant information available in data-rich domains to enhance the data-sparse domains has emerged as an important research focus in the industry. The traditional cross-domain [1,9,14,16,19,25,28,31] recommendation can be categorized into two paradigms: sample transfer and parameter transfer from the well-trained source model.…”