TLDR: This research introduces HeteroIR and HeteroIM, two novel models designed to enhance user recommendation systems. Unlike traditional methods that focus solely on user interaction or influence maximization, these models simultaneously consider both the likelihood of a user accepting an invitation and their potential to further spread information. By integrating ‘spread capability’ and ‘interaction willingness,’ HeteroIR and HeteroIM aim to create more effective recommendations that foster broader information propagation. The models demonstrated significant performance improvements in experiments and were successfully deployed in Tencent’s online gaming platforms, yielding substantial gains in user engagement and information dissemination.
User recommendation systems are a common feature in many online platforms, from social media to e-commerce and online gaming. Their primary goal is to boost user engagement by encouraging users to invite others to interact, which can lead to a wider spread of information. Think of it like a friend inviting you to a new game or a social event – it often works better than a generic advertisement.
However, current recommendation methods face a dilemma. Traditional systems are great at predicting if someone will accept an invitation (interaction willingness), but they often miss the bigger picture: how much that invited person can then spread the information further. On the other hand, methods designed for “Influence Maximization” (IM) are excellent at identifying users who can spread information widely, but they don’t always consider if the initial invitation will even be accepted.
This creates a significant challenge: how do you recommend users who are not only likely to interact but also have the potential to become powerful spreaders themselves, creating a ripple effect across the network?
Introducing HeteroIR and HeteroIM
To address this, researchers have proposed two innovative models: HeteroIR (Heterogeneous Influence-based Recommendation) and HeteroIM (Heterogeneous Influence-Maximization Recommendation). These models aim to bridge the gap between maximizing information spread and ensuring user interaction willingness.
HeteroIR offers a straightforward approach to unlock the spreading potential within user recommendation systems. It uses a two-stage framework to estimate the ‘spread profits’ of a recommendation. This means it doesn’t just look at the immediate interaction but also at the potential for that interaction to lead to further spread. For example, if inviting user ‘A’ leads to ‘A’ inviting five more people, that’s a higher spread profit than inviting user ‘B’ who doesn’t invite anyone else.
HeteroIM takes this a step further by integrating influence maximization directly into the recommendation process. It focuses on incrementally selecting the most influential invitees to recommend. A key concept here is the ‘Reverse Reachable (RR) set,’ which helps identify users who can be reached through propagation. HeteroIM then re-ranks candidates based on ‘Shared RR sets,’ ensuring that the recommendations not only have high spread capability but also a strong likelihood of interaction, and importantly, avoiding recommending multiple people who would spread to the exact same audience.
Real-World Impact and Performance
The effectiveness of HeteroIR and HeteroIM was rigorously tested and compared against existing state-of-the-art methods across various datasets. The results were highly promising, showing that these new models significantly outperform baselines in terms of both spread coverage and recommendation accuracy.
Perhaps the most compelling validation comes from their real-world deployment. Both HeteroIR and HeteroIM were implemented in Tencent’s online gaming platforms. The online A/B tests demonstrated remarkable improvements: HeteroIR led to an 8.5% improvement, and HeteroIM achieved a 10% improvement in key online metrics. These metrics included the secondary invite rate (how often invited users then invite others), secondary invite times (the average number of times invitees spread information), and reach retain rate (the ratio of invited users who log in the next day).
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Why This Matters
This research highlights a crucial shift in how we think about user recommendations. It’s no longer just about getting a user to click or accept an invitation. It’s about understanding and leveraging the full potential of social networks for information dissemination. By combining interaction willingness with spread capability, HeteroIR and HeteroIM offer a more holistic and effective approach to user recommendation, leading to broader information propagation without incurring additional advertising costs.
For more technical details, you can refer to the full research paper: Heterogeneous Influence Maximization in User Recommendation.
