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Apr 8, 2024 · Artificial Intelligence

What Will Recommendation Systems Look Like in 2026? Emerging Trends and Challenges

This article analyzes the current bottlenecks of conventional recommendation systems and outlines ten forward‑looking research directions for 2026, including retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value estimation, site‑wide optimization, interactive recommendation, personalized modeling, decision‑theoretic framing, and the integration of large language models via the OneRec framework.

Large Language ModelsUser Retentioninteractive recommendation

0 likes · 18 min read

What Will Recommendation Systems Look Like in 2026? Emerging Trends and Challenges

DataFunTalk

Apr 3, 2024 · Artificial Intelligence

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

This presentation outlines the current bottlenecks of conventional recommendation pipelines and proposes a 2026 roadmap that includes retention improvement, user‑growth strategies, content‑ecosystem metrics, Pareto‑optimal multi‑objective optimization, long‑term value modeling, site‑wide spatial optimization, interactive recommendation, personalized modeling, and the integration of large‑model fusion through the OneRec framework.

Large Language ModelsRecommendation SystemsUser Retention

0 likes · 18 min read

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

DataFunTalk

Feb 26, 2023 · Artificial Intelligence

Interactive Recommendation System for Meituan Food Delivery: Architecture, Challenges, and Evaluation

This article details Meituan's interactive recommendation system for its food‑delivery homepage feed, covering the motivation, challenges, system architecture, user intent modeling, evaluation metrics, experimental results, and future directions, illustrating how real‑time, user‑centric recommendations improve conversion and user experience.

Meituanfood deliveryinteractive recommendation

0 likes · 25 min read

Interactive Recommendation System for Meituan Food Delivery: Architecture, Challenges, and Evaluation

Meituan Technology Team

Feb 16, 2023 · Artificial Intelligence

Interactive Recommendation System for Food Delivery Feed

This article details Meituan Waimai's end‑to‑end interactive recommendation system for the food‑delivery homepage feed, explaining its architecture, trigger strategies, recall and ranking pipelines, evaluation metrics, experimental results, and future optimization directions.

Evaluation MetricsMeituanfood delivery

0 likes · 24 min read

Interactive Recommendation System for Food Delivery Feed

DataFunSummit

Mar 15, 2022 · Artificial Intelligence

KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research

The article introduces KuaiRec, a densely observed short‑video recommendation dataset with 99.6% density covering 1,411 users and 3,327 videos, discusses its structure, advantages over sparse public datasets, and its applicability to unbiased, interactive, conversational and reinforcement‑learning based recommendation studies.

KuaiRecRecommendation Systemsdense dataset

0 likes · 7 min read

KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research

DataFunTalk

Mar 4, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article presents the design and implementation of interactive recommendation and travel‑theme recommendation in Alibaba's Fliggy app, covering background, user demand classification, real‑time interest capture, various recall strategies, ranking models, multi‑task learning, and engineering tricks to improve CTR and user experience.

Fliggyaiinteractive recommendation

0 likes · 16 min read

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

DataFunSummit

Feb 7, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article explains how Fliggy combines interactive recommendation with travel‑theme recommendation, detailing the underlying algorithms, user‑demand classification, real‑time interest capture, recall strategies, multi‑task learning for CTR prediction, and engineering tricks that improve personalization and click‑through rates.

AlibabaFliggyRecommendation Systems

0 likes · 17 min read

Alibaba Cloud Developer

Feb 19, 2019 · Artificial Intelligence

How Interactive Recommendation Boosts User Engagement: The “Wind Vane” Framework Explained

This article explores the design and implementation of an interactive recommendation system—dubbed “Wind Vane”—that enhances user experience by prompting keyword queries, leveraging search logs, meta‑path recall, and a custom Attention‑GRU ranking model, with detailed analysis of recall, sorting, display control, and real‑world performance during Alibaba’s 2018 Double‑11 event.

attention GRUe‑commerceinteractive recommendation

0 likes · 20 min read

How Interactive Recommendation Boosts User Engagement: The “Wind Vane” Framework Explained