Event Recommendation Engine for Cloud Software Management
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Solution Overview
Problem
Organizations face challenges with installing, maintaining, and updating software on multiple computer systems, leading to compatibility issues and resource inefficiencies, prompting a shift towards on-demand cloud computing services for easier access to shared resources and software.
Innovation Solution
Implementing an on-demand database service environment that allows users to access software and resources via the internet, utilizing a multi-tenant database system to manage and provide cloud-based services, including tracking updates and user actions, and recommending events based on collaborative filtering methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If software is installed on organization's computer systems, then software functionality is provided, but installation and maintenance require significant time commitments and resources
Solution Approach 1:
The patent introduces an event recommendation system as an intermediary layer between the user and the on-demand service platform. This system analyzes user behavior patterns and automatically recommends relevant events, reducing the time users need to spend searching for and selecting services. The recommendation engine acts as a mediator that bridges user needs with available services, eliminating manual browsing and selection time.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and pre-generates event recommendations before users actively search for services. By anticipating user needs based on historical data and behavior analysis, the system prepares relevant service recommendations in advance, reducing the time users spend on service discovery and selection.
2Adaptability or versatility
If software is installed on multiple computer systems, then software availability is improved, but compatibility issues and version management become complex
Solution Approach 1:
The event recommendation system serves as an intermediary that abstracts away the complexity of multiple system configurations. Instead of directly managing software across numerous systems, the system recommends events based on user behavior patterns, eliminating the need for direct software installation and version management on each individual system.
Solution Approach 2:
The system uses behavioral data copies and patterns rather than directly deploying software across multiple systems. By analyzing and replicating successful user behavior patterns across different users and contexts, the system provides consistent service recommendations without requiring identical software installations on each system.
3Loss of time
If on-demand cloud computing services are used, then software management time is reduced, but personalized service recommendations require advanced tracking and analysis
Solution Approach 1:
The recommendation system operates autonomously by automatically tracking user behavior patterns and generating recommendations without requiring manual intervention. The system self-manages the complex tasks of data collection, analysis, and recommendation generation, reducing the burden on users while handling the analytical complexity through automated processes.
Solution Approach 2:
The system implements continuous feedback loops where user interactions with recommended events are tracked and fed back into the analysis engine. This feedback mechanism refines behavior patterns and improves recommendation accuracy over time, managing the complexity through iterative learning rather than static complex rules.
Data Source
AI summary
Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an intent field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data tables having a set field, a target event field, and a similarity score field.