User Modeling via Latent Preference Quantification
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Solution Overview
Problem
Existing media recommender systems struggle to quantify user interests in media items, failing to account for opportunity costs, which limits their ability to simulate user behavior and test recommendation policies effectively.
Innovation Solution
Modeling users as Partially Observable Markov Decision Processes (POMDPs) to infer latent preferences and opportunity costs from historical data, using an iterative Monte-Carlo Expectation Maximization procedure to generate user models that quantify predicted interests in media items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommender systems are used to rank media items, then items can be ordered by correlation with user preferences, but the systems cannot quantify the actual interest level or opportunity cost for individual items
Solution Approach 1:
The patent transforms the recommendation problem from simple ranking to quantifying latent preference parameters. By modeling user interest as a parameter θ that can take values from 0 to T (total time), the system achieves precise measurement of user interest levels while maintaining manageable model complexity through parameterized representations.
Solution Approach 2:
The patent introduces a latent variable θ (user interest parameter) as an intermediary between observable user actions and the underlying preference structure. This intermediary enables the system to quantify interest levels without directly observing them, resolving the contradiction between measurement precision and model complexity.
2Reliability
If user behavior is modeled to capture sequential decision making, then opportunity costs can be accounted for, but the modeling and inference become computationally intensive
Solution Approach 1:
The patent performs preliminary action by pre-defining the utility function structure and possible action sequences before actual inference. By establishing the framework of opportunity costs and sequential decisions in advance, the system can efficiently process specific user cases without repeating complex modeling work, thus improving productivity while maintaining reliability.
Solution Approach 2:
The patent applies partial action by focusing inference on the specific parameter θ of interest rather than modeling all aspects of user behavior equally. This selective approach captures essential sequential decision-making characteristics while avoiding the computational burden of comprehensive behavior modeling, balancing reliability and productivity.
3Measurement precision
If Monte-Carlo Expectation Maximization is used to infer user parameters, then accurate latent preferences can be obtained, but the computational cost increases significantly
Solution Approach 1:
The patent uses partial action in the EM algorithm by focusing iterations on estimating the specific parameter θ rather than comprehensively optimizing all model parameters. This selective estimation achieves sufficient measurement precision for user interest quantification while reducing computational resource consumption compared to full parameter optimization.
Solution Approach 2:
The patent employs Monte-Carlo sampling as a copying mechanism, where multiple simulated trajectories are generated to approximate the expectation step. These sampled copies enable accurate latent preference estimation without requiring computationally intensive exact calculations, thus balancing precision with resource efficiency.
4Reliability
If AB tests are conducted to evaluate recommendation policies, then real user behavior can be observed, but the testing process is costly and time-consuming
Solution Approach 1:
The patent creates simulated user copies based on the inferred parameter θ to evaluate recommendation policies. These synthetic users replicate real user behavior patterns, enabling policy testing without conducting actual AB tests. This copying approach maintains evaluation reliability while eliminating the time loss associated with real-world testing.
Solution Approach 2:
The patent performs preliminary action by inferring user parameters θ from historical data before policy evaluation. This pre-processing step creates a foundation for simulated testing, allowing policies to be evaluated on synthetic users in advance of any real AB tests, thus reducing both time and resource requirements while maintaining reliability.
Data Source
AI summary
A method of recommending media items to a user is provided. The method includes receiving historical data for a user of a media providing service. The historical data indicates past interactions of the user with media items. The method includes generating a model of the user. The model includes a first set of parameters, each of the first set of parameters quantifying a predicted latent preference of the user for a respective media item provided by the media providing service. The method includes evaluating the predicted latent preferences of the user for the respective media items against the historical data indicating the past interactions of the user with the media items provided by the media providing service. The method includes selecting a recommender system from a plurality of recommender systems using the model of the user, including the first set of parameters. The method includes providing a media item to a second user using the selected recommender system.