Behavioral Recommendation Algorithm Using Temporal Pattern Mining
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Solution Overview
Problem
Conventional data mining approaches, such as content-based search, face challenges with noisy and biased results due to text dependency and domain specificity, particularly when handling similar content in different languages or with misspelled words, making it difficult to provide accurate and efficient recommendations.
Innovation Solution
A text-independent and domain-independent algorithm that analyzes user navigation information from access logs using temporal parameters to generate an optimal list of recommended items, focusing on user behavior patterns rather than content context, employing a recommendation system with a temporal pattern mining module, graph execution module, and random walk module to compute relevance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content-based search is used to mine data, then the system can provide recommendations based on document content, but the results become noisy and biased due to text dependency and domain specificity
Solution Approach 1:
The patent segments the recommendation system into two independent components: (1) a behavior pattern extraction module that mines user navigation sequences from access logs, and (2) a recommendation generation module that uses temporal pattern mining and random walk algorithms. This segmentation separates the recommendation logic from content-specific processing, enabling domain-independent operation while maintaining accuracy through behavioral signals.
Solution Approach 2:
The patent implements a universal recommendation framework that processes different types of queries and domains using the same core algorithms. The temporal pattern mining module and random walk-based ranking system handle diverse content types (images, text, videos from different languages) uniformly by relying on user behavior patterns rather than content-specific features, achieving both accuracy and domain independence.
2Adaptability or versatility
If conventional data mining approaches are used, then the system can process user-generated content, but it struggles with contextual differences and ambiguity in natural language processing across different languages
Solution Approach 1:
The patent introduces user behavior data as an intermediary that mediates between the query and the recommended items. Instead of directly processing and comparing content across different languages and contexts, the system uses user navigation patterns from access logs as a language-neutral bridge. This behavioral intermediary captures implicit contextual understanding without requiring explicit natural language processing, enabling accurate cross-language recommendations.
3Reliability
If the algorithm focuses on text-dependent content analysis, then it can understand document meaning, but it becomes biased and noisy due to misspelled words and contextual variations
Solution Approach 1:
The patent replaces the mechanical text-processing system with a behavior-based system. Instead of using complex natural language processing mechanisms to parse and understand text content, the system substitutes this with analysis of user navigation behavior patterns. This substitution eliminates the reliability issues associated with text processing (misspellings, contextual variations) while avoiding the complexity of sophisticated NLP algorithms, achieving consistent recommendations through simpler behavioral analysis.
Data Source
AI summary
Methods and system for providing a list of recommended items in response to a query includes obtaining user navigation information relevant to the query from a plurality of user access logs associated with the plurality of users. The user access logs define the navigation sequence followed by a plurality of users for a plurality of queries. The navigation sequence represents the users browsing behavior and identifies answers to the queries. A set of temporal parameters for analyzing the user navigation information is identified. The temporal parameters define time frame for analyzing the user navigation information. A list of recommended items to navigate is dynamically generated by analyzing user navigation information obtained from user access logs using temporal parameters. The generated list includes items that are most relevant to the query. The generated list of items is returned in response to the query and represents an optimal result.