Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
Quick Take
This study models user interaction with AI-driven recommendation systems, focusing on optimizing communication and recommendation set sizes to maximize user utility. It identifies a hybrid regime for optimal message precision and recommendation quantity, depending on cost parameters and sampling distributions, enhancing user experience in high-dimensional preference spaces.
Key Points
- Models user-AI interaction to optimize recommendation set size and communication cost.
- Identifies a hybrid regime for efficient information exchange and recommendation provision.
- Explores two sampling distributions: Bayes' posterior belief and optimized tilted distribution.
- Maximizes expected user payoff in high-dimensional product preference spaces.
- Demonstrates that optimal policies favor the less costly option between communication and search.
Article Content
From source RSS / original summaryarXiv:2605. 23944v1 Announce Type: new Abstract: We model the interaction between a user and an AI driven recommendation system. The user initiates the process by conveying preference information through a costly and noisy message. The AI assistant, acting as a Bayesian agent, interprets the user's message to form a posterior belief about their true preferences and make product recommendations.
In particular, it determines how many recommendations to present so as to maximize the user's expected utility from their final choice, while accounting for the search cost induced by the size of the recommendation set.
We use mutual information based cost functions to model the two distinct costs incurred by the user during the interaction: (i) a communication cost, which increases with the precision of their preference message, and (ii) a search cost, which increases with the size of the recommendation set provided by the AI assistant. We study products and preferences which live in d dimensional space, and ask how the user's expected payoff can be maximized.
For large d, we characterize how optimal message precision and recommendation set size depend on the cost parameters, under two distinct distributions from which recommendations can be sampled from the product universe: (i) Bayes' posterior belief, and (ii) an optimized tilted distribution.
Under the posterior sampling scheme (i), we identify a hybrid regime, in which an efficient interaction policy requires jointly optimizing the amount of information (in bits) conveyed by the user and the number of recommendations provided by the AI assistant. In the tilted sampling scheme (ii), our results show that the optimal interaction policy uses only one of communication and search, favoring whichever of them is less costly.
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