An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Authors:
Lei Shi,
Di Wang,
Harry Tran,
Helsing Xu,
Yuchen Lu,
Dhara Ghodasara,
Wilson Chaney,
Xueting Liao,
Jerry Yu,
Huayu Ding,
Reza Mirghaderi,
David Fan,
Qi Guo,
Chongguang He,
Warren Wang,
Warren Deng,
Mingze Gao,
Shike Mei,
Shuo Tang,
Zhe Zhang,
Jianming He,
Abhishek Kumar,
Haotian Wu,
Hamed Firooz,
Li Li
Abstract:
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the reasoning capa…
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Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the reasoning capability to naturally process unstructured or heterogeneously formatted contextual information. Incorporating such signals typically requires feature engineering, bespoke data pipelines, and carefully tuned heuristics. In this paper, we present an LLM-powered agentic recommendation system designed for Connected TV (CTV) content discovery that addresses these limitations. Our system leverages the reasoning capabilities of large language models to naturally process and synthesize diverse signals across varying schemas and structures, eliminating much of the manual integration inherent in traditional ranking and retrieval systems. Recognizing that current LLM-based solutions still fall short of traditional machine learning models in several recommendation tasks, including retrieval efficiency, personalization precision, and scalability, we adopt an agentic architecture that orchestrates specialized components, allowing each sub-task to be handled by the most suitable method, whether LLM-based or traditional ML. The main contribution of this work is our engineering approach to successfully overcoming the practical limitations of enabling LLM for recommendation, particularly inference latency. We share insights from our work and discuss the trade-offs and lessons learned in building a hybrid system that combines the flexibility of LLMs with the performance of established recommendation techniques.
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Submitted 22 July, 2026; v1 submitted 10 July, 2026;
originally announced July 2026.
Foundation for Frequent Pattern Mining Algorithms Implementation
Authors:
Prof. Paresh Tanna,
Dr. Yogesh Ghodasara
Abstract:
As with the development of the IT technologies, the amount of accumulated data is also increasing. Thus the role of data mining comes into picture. Association rule mining becomes one of the significant responsibilities of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. The frequent pattern mining algorithms determine the frequent patter…
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As with the development of the IT technologies, the amount of accumulated data is also increasing. Thus the role of data mining comes into picture. Association rule mining becomes one of the significant responsibilities of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. The frequent pattern mining algorithms determine the frequent patterns from a database. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including majors are Apriori, Direct Hashing and Pruning, FP-Growth, ECLAT etc. The aim of this study is to analyze the existing techniques for mining frequent patterns and evaluate the performance of them by comparing Apriori and DHP algorithms in terms of candidate generation, database and transaction pruning. This creates a foundation to develop newer algorithm for frequent pattern mining.
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Submitted 7 February, 2014;
originally announced February 2014.