Mobile Activity Recommendation System Context Matching
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Solution Overview
Problem
Users of mobile devices face challenges in finding relevant mobile applications and Websites due to the large number of options available, exacerbated by the limitations of traditional keyword-based search engines on small screens and the lack of interaction between mobile applications.
Innovation Solution
An activity recommendation system (ARS) that processes content items to determine semantic information, such as entities and categories, to recommend relevant mobile applications and Websites based on the user's context, including currently accessed content and device information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional keyword-based search engines are used on mobile devices, then users can search for applications, but the small screen size and limited input modalities make it cumbersome to type queries and browse through dozens of results
Solution Approach 1:
The system automatically monitors mobile device context and generates activity recommendations without requiring user input. The context monitoring module continuously tracks device state, location, and running applications, while the recommendation module autonomously processes this data and presents relevant activity suggestions, eliminating the need for manual keyword searching.
Solution Approach 2:
The system establishes a feedback loop where the context monitoring module continuously provides device state information to the recommendation module, which then generates updated recommendations based on current context. This real-time feedback mechanism ensures recommendations adapt dynamically to changing user needs without requiring re-searching.
2Adaptability or versatility
If the number of mobile applications increases to provide more options, then users have more choices, but users face the problem of searching through an increasing number of applications to find relevant ones
Solution Approach 1:
The recommendation module acts as an intermediary between the user and the large ecosystem of mobile applications. Instead of directly searching through numerous applications, the user receives curated recommendations that filter and prioritize relevant options based on device context, effectively mediating the interaction between user needs and application availability.
Solution Approach 2:
The system extracts only the most relevant application recommendations from the large pool of available applications by analyzing device context. The recommendation module filters out irrelevant options and presents a condensed set of highly relevant suggestions, extracting value from the broader application ecosystem without requiring users to navigate through all options.
3Reliability
If there is no interaction between mobile applications, then each application operates independently, but it is not convenient or efficient to analyze information in one application and use it to perform functions in another application
Solution Approach 1:
The context monitoring module serves multiple functions: it tracks device location, identifies running applications, monitors device state, and collects contextual information. This multi-functional component enables the system to gather comprehensive data across applications and generate recommendations that leverage information from any source, facilitating efficient cross-application information usage while maintaining application independence.
4Measurement precision
If manual keyword searching is required, then users can find specific applications, but it is cumbersome to type queries and browse through dozens of results on a mobile device
Solution Approach 1:
The recommendation system performs self-service by automatically analyzing device context and generating relevant activity recommendations without requiring user typing or manual searching. The system monitors device state, location, and application usage patterns to autonomously identify and present relevant applications, eliminating the need for manual keyword input while maintaining precise matching.
Solution Approach 2:
The context monitoring module continuously pre-processes device information in the background, maintaining an updated understanding of device state, location, and running applications. This preliminary action ensures that when recommendations are needed, the system already has processed contextual data ready for immediate use, eliminating the need for users to initiate searches.
Data Source
AI summary
Techniques for recommending mobile device activities, such as accessing mobile applications and/or mobile Web pages, are described. Some embodiments provide an Activity Recommendation System (“ARS”) configured to recommend relevant activities for a user to perform with a mobile device, based on context of the mobile device. In one embodiment, the ARS recommends mobile applications based content items (e.g., Web pages, images, videos) that are being currently accessed via the mobile device. The ARS may process information about mobile applications and content items to determine semantic information, such as entities and/or categories referenced or associated therewith. The ARS may then use the semantic information to determine mobile applications that have semantic information that is at least similar to that of a content item accessed via a mobile device.