Context Sequence Aware Recommendation Model Using Bigram Segmentation
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Solution Overview
Problem
Existing recommendation systems face challenges in efficiently incorporating both context and sequence information, leading to issues like data scarcity, scalability problems, and limited diversification in recommendations, particularly in training scenarios where expertise levels and transitions are complex.
Innovation Solution
A context and sequence aware recommendation system that uses a processor-based module to access historical training data, identify contextual keywords and item bigrams, and apply a collapsed Gibbs sampling technique to generate a recommendation model, incorporating user role and training sequences to recommend subsequent trainings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequentist recommendation methods such as Markov models are used to incorporate context and preference sequences, then recommendation accuracy is improved, but state space explosion occurs leading to scalability problems
Solution Approach 1:
The patent extracts only the necessary sequential information through item bigrams (pairs of consecutive items) rather than considering all possible state transitions. This selective extraction maintains recommendation accuracy by capturing sequential patterns while avoiding the combinatorial explosion of full state spaces in Markov models.
Solution Approach 2:
The patent segments the sequence information into fixed-length bigrams (pairs of consecutive items) rather than modeling the entire sequence as a single state. This segmentation reduces complexity by breaking down the state space into manageable, localized patterns that can be processed efficiently.
2Adaptability or versatility
If traditional context aware recommendation models are used, then contextual information is incorporated, but data scarcity problems including cold-start issues occur
Solution Approach 1:
The patent merges two approaches: it combines the contextual information handling of traditional context-aware models with the sequential pattern capture of bigram-based methods. This hybrid approach leverages both context keywords and item sequences, making the system more robust to data scarcity by having multiple signals to work with rather than relying on a single data source.
3Ease of operation
If collaborative filtering methods are used to identify user interests, then personalized recommendations are generated, but the portfolio problem occurs where a single narrow interest is identified
Solution Approach 1:
The patent adds a sequential dimension to traditional context-aware recommendation by incorporating item bigrams. This transforms the recommendation from considering only contextual features to considering both context and sequential patterns, creating a more nuanced view of user interests that captures evolution over time rather than a single static profile.
4Adaptability or versatility
If recommendation systems increase diversification to improve selectiveness, then variety in recommendations is improved, but recommendation accuracy decreases
Solution Approach 1:
The patent introduces dynamics by modeling user interests as evolving sequences rather than static profiles. The item bigram approach captures how user preferences change over time through sequential patterns, allowing the system to adapt recommendations dynamically based on recent behavior while maintaining accuracy through the structured sequential modeling.
Data Source
AI summary
The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.