An iptv program recommendation method and system based on multi-source data

By leveraging multi-source data governance and deep learning algorithms, combined with the dual-tower model and the Wide&Deep model, we have achieved efficient and accurate program recommendations on the IPTV platform. This has solved the problem of incomplete user interest representation and improved recommendation effectiveness and user experience.

CN122513623APending Publication Date: 2026-08-04海看网络科技(山东)股份有限公司
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Patent Information

Application Number
CN202611009232.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-07-08
Publication Date
2026-08-04

AI Technical Summary

Technical Problem

Existing IPTV platforms lack the ability to integrate multi-source data and effectively mine algorithms, resulting in one-sided user profile construction, failing to fully reflect users' true interests in home scenarios, and leading to poor recommendation performance.

Method used

By collecting heterogeneous data from multiple sources, performing unified cleaning and standardization, a data foundation is constructed. Combined with deep learning recommendation algorithms, user features are calculated in real time. A hierarchical architecture of a dual-tower model and a Wide&Deep model is used for recall and ranking. Incremental training is driven by real-time collection of user feedback data to achieve self-optimization of the recommendation model.

Benefits of technology

It significantly improves the intelligence level and user experience of the IPTV recommendation system, achieves efficient and accurate program recommendations, and solves the problems of inconsistent quality of multi-source data and the timeliness of user interests.

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Abstract

The application discloses an IPTV program recommendation method and system based on multi-source data, and mainly relates to the technical field of television program recommendation. The method comprises the following steps: collecting multi-source heterogeneous data, managing the multi-source heterogeneous data, and constructing a data base; based on a batch processing engine, extracting historical data from the data base and persistently storing the historical data into a feature vector database; accessing behavior logs reported by a set top box in real time, and writing real-time features extracted into a cache; based on full training data in the data base and feature vectors in the feature vector database and the cache, outputting a personalized program recommendation prediction result for a target user; and dynamically adjusting parameters of the recommendation model according to feedback behavior data of the target user for the recommended content, so as to realize continuous self-optimization of the recommendation effect. The application has the beneficial effect that it combines a deep learning recommendation algorithm, realizes real-time modeling of user interest, and accurately recommends programs.
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