Diversifying by Intent in Recommender Systems
(2405.12327)Abstract
It has become increasingly clear that recommender systems overly focusing on short-term engagement can inadvertently hurt long-term user experience. However, it is challenging to optimize long-term user experience directly as the desired signal is sparse, noisy and manifests over a long horizon. In this work, we show the benefits of incorporating higher-level user understanding, specifically user intents that can persist across multiple interactions or recommendation sessions, for whole-page recommendation toward optimizing long-term user experience. User intent has primarily been investigated within the context of search, but remains largely under-explored for recommender systems. To bridge this gap, we develop a probabilistic intent-based whole-page diversification framework in the final stage of a recommender system. Starting with a prior belief of user intents, the proposed diversification framework sequentially selects items at each position based on these beliefs, and subsequently updates posterior beliefs about the intents. It ensures that different user intents are represented in a page towards optimizing long-term user experience. We experiment with the intent diversification framework on one of the world's largest content recommendation platforms, serving billions of users daily. Our framework incorporates the user's exploration intent, capturing their propensity to explore new interests and content. Live experiments show that the proposed framework leads to an increase in user retention and overall user enjoyment, validating its effectiveness in facilitating long-term planning. In particular, it enables users to consistently discover and engage with diverse contents that align with their underlying intents over time, thereby leading to an improved long-term user experience.
Overview
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The paper proposes a new approach to diversify recommender systems by incorporating user intents, focusing on long-term user satisfaction instead of short-term metrics like clicks.
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It introduces an intent-based diversification framework that uses machine learning to predict user intents and a probabilistic model to select a variety of recommended items that satisfy predicted intents.
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The approach has been tested on a large content recommendation platform, showing improvements in user enjoyment, daily active users, and engagement with novel content.
Diversifying by Intent in Recommender Systems
Introduction
Recommender systems form the backbone of many online platforms, helping users discover content that aligns with their interests. Traditionally, these systems have been designed to maximize short-term metrics such as clicks and likes. However, there’s a growing understanding that this approach can inadvertently harm the long-term user experience. Think about it—clickbait articles or overly repetitive recommendations can turn users off after a while.
This paper presents a fascinating approach to tackle this challenge by incorporating user intents into the recommendation process. By understanding and predicting a user’s underlying intents that persist across multiple visits, the system can diversify recommendations in a way that enhances long-term satisfaction. Let's dive in and see how they achieved this.
Core Concept: Intent-Based Diversification
The key idea of the paper is to use user intents to inform the final recommendation stage. User intents are the underlying motivations behind why users behave the way they do on a platform—such as seeking entertainment, learning something new, or exploring unfamiliar content.
- Intent Prediction: The system first predicts the user's intent using a machine learning model that analyzes signals such as past interactions, time of day, and session length.
- Diversification Framework: Armed with these intent predictions, the system employs a probabilistic model to diversify the recommended items. This model makes sure that items covering different intents appear on the same recommendation page.
The authors proposed a clever algorithm for diversification, which calculates the probability that each item will satisfy the user's predicted intents. By sequentially picking items that maximize this probability, they ensure a diverse and relevant set of recommendations. This approach helps in achieving a balance between showing highly relevant items while also introducing new and varied content that aligns with different user intents.
Strong Numerical Results
The framework was tested on a massive content recommendation platform that serves billions of users daily. Here are some key findings from their live A/B tests:
- User Enjoyment: There was a significant increase of 0.09% in overall user enjoyment.
- Daily Active Users (DAU): DAU improved by 0.05%, suggesting that users were more likely to return to the platform regularly.
- Diversity and Exploration: Users engaged more with novel content providers, indicating a higher quality of exploration.
While these numbers may seem modest, they translate to substantial improvements given the scale of the user base involved.
Implications and Future Directions
The approach described in the paper has several practical implications:
- Balanced User Experience: By balancing short-term satisfaction with long-term engagement, the framework addresses two often competing goals.
- Dynamic Adaptation: The system adapts to the user's changing intents, ensuring a personalized and evolving recommendation experience.
Theoretically, this work opens the door to more comprehensive user models that consider long-term behaviors and outcomes. Future research could explore the integration of more intricate intent taxonomies or the application of this approach to other types of recommender systems, such as e-commerce or social media.
Conclusion
This paper makes a compelling case for incorporating user intents into the recommender system to optimize for long-term user satisfaction. Through a well-thought-out probabilistic diversification algorithm, it manages to improve key metrics such as user enjoyment and DAU, while also encouraging users to explore new content.
As AI continues to advance, frameworks like these will likely become a staple in the design of smarter, more user-centric recommendation systems. The next steps could involve refining the intent prediction models or expanding the types of intents to cover more user motivations.
Overall, this work is a significant step towards making our digital experiences richer and more fulfilling in the long run.
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