Algorithmic advances in smart TV content recommendation: a structured evidence-mapping review

Smart TV recommenders operate under constraints that are less prominent on personal devices: viewing is often passive, one account may represent several viewers, direct feedback is scarce, and programmes are long and semantically rich. We mapped research published from 2015 to 2025 using a structured evidence-review protocol. The submitted bibliography comprised 122 DOI-bearing references, including two foundational sources and 120 records used in the evidence map. Crossref verified 116 records; six stable arXiv DOI records were retained. Searches across six sources yielded 2,526 database-level results. A combined deduplication and title/abstract screening stage removed 2,414 records, leaving 112 unique records for full-text assessment; all 112 full texts were retrieved, 87 records were excluded, and 25 met the eligibility criteria. These 25 studies were combined with the 95-study initial corpus to form the 120-study evidence map. Separately, we conducted an independent dual-reviewer eligibility audit of 126 records, comprising all 120 included studies and six representative boundary exclusions. Agreement was 97.6% (123/126; Cohen's κ = 0.788). Six records were excluded by both reviewers, and three discordant judgments were resolved by joint full-record review. For analysis, each study was assigned one primary technical category and one of six mutually exclusive primary functional goals; secondary technical labels captured hybrid methods. Evidence reporting was profiled separately for data transparency, reproducibility, evaluation design, external validation, and deployment validation. The final literature corpus encompasses intelligent recommendation systems based on deep learning, sequence analysis, graph theory, shared accounts, and multimodal approaches.