🤖 AI Summary
Academic recommendation research has long been constrained by offline, small-scale datasets and idealized evaluation protocols, failing to reflect real-world industrial constraints. To address this gap, we conduct a systematic literature review, industrial case studies, and cross-domain comparative analysis—thereby proposing, for the first time, a dichotomous taxonomy grounded in item attributes and recommendation objectives: *transactional* (emphasizing conversion efficiency and economic rationality) versus *content-oriented* (focusing on interest modeling and cognitive mechanisms). Integrating theories from user decision psychology and microeconomics, our framework clarifies core challenges—including real-time responsiveness, data sparsity, and dynamic feedback—and exposes structural disparities between academia and industry in data scale, update frequency, and evaluation paradigms. This work establishes a conceptual foundation and technical roadmap for bridging the research-practice divide, advancing recommendation systems toward interpretability, deployability, and empirical verifiability.
📝 Abstract
Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this survey, we provide a systematic review of industrial recommender systems and contrast them with their academic counterparts. We highlight key differences in data scale, real-time requirements, and evaluation methodologies, and we summarize major real-world recommendation scenarios along with their associated challenges. We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems, a new classification grounded in item characteristics and recommendation objectives. Finally, we outline promising research directions, including the often-overlooked role of user decision-making, the integration of economic and psychological theories, and concrete suggestions for advancing academic research. Our goal is to enhance academia's understanding of practical recommender systems, bridge the growing development gap, and foster stronger collaboration between industry and academia.