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Baidu AI Cloud Media and Entertainment China Translated from Chinese

Coocaa Network

Marketing Personalization

Coocaa needed to improve smart-TV retention by understanding both users and content at a scale too large for manual tagging and then matching viewers to the right programs.

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The catalog includes 16,000+ films, 4,000+ TV series and 3,100+ variety programs.

Reported impact

Business outcome

Coocaa made its TV experience more personalized and dynamic, improving content matching and giving the large-screen channel a stronger reason to retain viewer attention.

  • Customer Experience
  • Revenue Growth
  • Scalability

Solution

Coocaa Network and Baidu AI Cloud built a recommendation and content-understanding stack for smart TV, with Baidu's MCA media-content-analysis service acting as the content intelligence layer. Instead of depending on editors to watch and tag every title, the platform analyzes long-form video through scene classification, public-face recognition and Baidu knowledge-graph enrichment so movies, TV series, variety shows, animation, games and documentaries gain much richer metadata. That structured description is then paired with user-preference signals in Coocaa's smart-AI business module to power recommendation and, ultimately, a TV-side information flow tailored to each household. The result is a two-sided architecture: MCA and knowledge graph transform raw video into structured tags, while the recommendation layer uses those tags to match content to viewers across many verticals. In practice, that lets the TV experience feel less static and more personalized without requiring infeasible amounts of manual content labeling.

Data flow

Long-form video is analyzed by MCA and knowledge-graph services to generate structured tags, then those tags are combined with user-preference signals in the recommendation system to build personalized TV-side information streams.

Solution architecture

4 components · 2 layers
  1. Compute
    • Content Analysis Provides the high-level content-understanding layer for smart-TV recommendation.
    • MCA Media Content Analysis Extracts tags from long-form video across scene, people and semantic dimensions.
    • Public Face Recognition Identifies celebrities and public figures appearing in video content.
  2. Storage
    • Knowledge Graph Adds structured metadata such as series names, actors, roles and genres to video assets.

Architecture clues

  • Recommendation is built on both user understanding and content understanding
  • The outcome is a TV-side information flow rather than a static catalog
  • The system combines scene classification, face recognition and knowledge-graph enrichment

Evidence from the source

Baidu MCA and the recommendation system enabled a TV-side information flow so each user sees the content they like when they turn on the TV (original: 基于智能内容推荐系统，以及MCA媒体内容分析服务，为酷开网络创造性地推出了电视端信息流……映入到眼前的就是自己喜欢看的内容).
Coocaa decided to embrace AI and focus on smart-TV systems and value-added operating services (original: 最终选择拥抱AI技术，聚焦智能电视系统研发和智能电视运营增值服务两大领域).
Its content catalog already includes more than 16,000 films, 4,000 TV series and 3,100 variety shows, making manual tagging infeasible (original: 用户可观看的电影达16000+、电视剧4000+、综艺3100+，靠人工手动打标签，根本不现实).
Transparency Evidence & methodology 70/100 reference value · 82% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Coocaa Network customer story

    First-party
    https://cloud.baidu.com/customer/case/kukaiwangluo.html
    Source type
    Vendor-published customer story
    Published
    May 15, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 15, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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