Location-Based Audiovisual Content Recommendation System
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Solution Overview
Problem
Subscribers face difficulty in determining what audiovisual content to watch due to the vast amount of available content, and existing electronic program guides (EPGs) may not effectively recommend content based on the subscriber's interests.
Innovation Solution
A method and device that utilize location information from a mobile device with GPS functionality to predict a subscriber's interests and recommend associated audiovisual content items, which are then presented to the subscriber for viewing or recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the amount of available audiovisual content is increased, then the variety and richness of content is improved, but the difficulty of selecting appropriate content increases
Solution Approach 1:
The system automatically generates content recommendations by analyzing subscriber behavior data, viewing patterns, and content metadata without requiring manual input from the subscriber. The recommendation engine self-adjusts and refines suggestions based on feedback from subscriber interactions, making the content selection process autonomous and reducing the burden on users to manually navigate vast content libraries.
Solution Approach 2:
The system implements a feedback loop where subscriber viewing behavior, ratings, and interactions with recommended content are continuously monitored and fed back into the recommendation algorithm. This feedback mechanism allows the system to learn from subscriber preferences and dynamically adjust content recommendations, improving accuracy over time and helping subscribers discover relevant content more easily.
2Device complexity
If traditional electronic program guides are used for content recommendation, then the system complexity is kept low, but the recommendation accuracy based on subscriber interests is insufficient
Solution Approach 1:
The system transitions from traditional two-dimensional EPG interfaces (time and channel) to a multi-dimensional recommendation space that incorporates subscriber behavior patterns, content metadata, viewing history, and preference profiles. This dimensional expansion enables much more precise and personalized content recommendations while maintaining manageable system complexity through modular architecture and automated processing.
Data Source
AI summary
Recommending audiovisual content items to a subscriber based on locations visited by the subscriber. Location information from a mobile device having GPS functionality is received. The location information includes locations the subscriber has visited with the mobile device. The various locations may be counted, ranked, and grouped. Interests of the subscriber are predicted based on the location information. One or more audiovisual content items associated with the predicted interest are selected. A determination regarding the predicted programming of interest being available is made. Item recommendations are generated for available ones of the selected audiovisual content items. A referral of the one or more item recommendations is presented to the subscriber. The subscriber may accept or reject the item recommendation within the referral and appropriate action may be taken including initiating presentation of the selected audiovisual content item on a display or recording of the associated audiovisual content item.