Systems and methods for capturing and processing screen-recorded user-specific recommended output, digital advertisements, and ai-generated mixed media

A computer-implemented method for analyzing and displaying personalized digital content, digital advertisements, online shopping behaviour, and AI generated mixed media related to a third-party recommender system, the method comprising:

• (a) receiving, on a computing device, content presented via a visual user interface of a third-party recommender system and associated personalized media content, including digital advertisements and generative media, wherein the content comprises at least one of:
• (i) a screen-recorded video depicting the rendered interface;
• or
• (ii) document object model (DOM) elements extracted from a web browser;
• (b) optionally recording, during step (a), audio commentary provided by the user during the screen recording session;
• (c) executing, under orchestration-server control, a computer-vision pipeline to extract an intermediate representation comprising:
• (i) image-text embeddings;
• (ii) OCR-derived structured text regions;
• and
• (iii) transcribed speech, aligned to video segments;
• (d) generating, on the computing device, metadata describing the personalized content, the metadata including at least:
• (i) a promotion-indicator flag set to TRUE when optical-character recognition detects any keyword selected from the group consisting of “Sponsored,” “AD,” “Promoted,” and “Shop” within a text region that overlaps the visual boundary of the media tile;
• (ii) a diversity-indicator flag set to TRUE when an asset identifier of a current segment is absent from a rolling cache of asset identifiers extracted from a predetermined number of immediately preceding segments;
• (iii) a similarity-indicator flag that is TRUE when, within a rolling window of N segments, a majority exhibit a cosine similarity ≥T1 in a unified image-text embedding space;
• (iv) a context-switch flag that is TRUE when a newly ingested segment diverges from a previous centroid by more than T2 and at least M subsequent segments align more closely with a new centroid;
• and
• (v) a recommender-system-output satisfaction indicator comprising, a self-reported representation score, a change-propensity score;
• and a comfort score, each supplied on a Likert scale;
• (e) receiving user-supplied tags and Likert-scale scores;
• (f) receiving, from the user via an interactive interface, one or more metadata tags associated with the content of the screen-recorded video or its AI-generated summary;
• (g) merging metadata, tags, summaries, and scores into a composite metadata package;
• (h) associating the user-supplied tags with the corresponding metadata generated in step (d), and integrating transcribed user commentary into the metadata structure;
• (i) transmitting the metadata of step (f), and optionally the depersonalized video, to a cloud-based storage or analytics service;
• (j) redacting PII, generating a verification indicator, and transmitting the package to a cloud-based service while deleting raw media;
• (k) visualizing the metadata in a dashboard that groups segments by consumer-profile type, orders such groups by change, and renders a representative “For-You Page” preview responsive to a user selection of the group name;
• wherein steps (b)-(h) are executed without transmitting raw video or unprocessed audio commentary, thereby preserving privacy and minimizing bandwidth usage.