What is it about?
In the world of online shopping and streaming, recommender systems generally fall into two categories. Sequential models analyze a user’s long-term history to understand their evolving tastes over months or years. Session-based models are designed for anonymous or new users, predicting the next click based only on a very short sequence of recent interactions (a single "session"). Historically, models that excelled at one task performed poorly at the other. This work introduces TriSeRec, a unified framework that achieves state-of-the-art performance in both scenarios simultaneously. TriSeRec works by looking at user behavior through three distinct "views" or lenses: 1. The Intra-Sequence View: It builds a graph of the specific user’s current items to capture immediate, short-term interests. 2. The Inter-Sequence View: It looks at global patterns of how all users transition between items to fill in gaps when data is scarce. 3. The Sequential View: It uses self-attention to understand the long-term order and dependencies in the user’s history. By using a novel learning module, the system extracts the unique strengths of each view and fuses them into a robust final prediction.
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Why is it important?
This research is significant for both the academic community and the industry for three main reasons: 1. Unified Efficiency: Previously, platforms often needed to maintain separate models for logged-in users (history-rich) and anonymous users (history-scarce). TriSeRec provides a "one-size-fits-all" solution, reducing engineering complexity, maintenance costs, and deployment effort. 2. Handling the "Cold Start": By incorporating a global view of item transitions, the model performs exceptionally well even when a user has clicked only a few items. This is crucial for retaining new users who haven't generated much data yet. 3. Accuracy Gains: The framework proves that combining Graph Neural Networks (which excel at local patterns) with Transformers (which excel at long-range patterns) creates a system that is greater than the sum of its parts. It outperforms existing specialized models on major benchmark datasets like Amazon and Yelp.
Perspectives
For years, the recommender systems community has debated the merits of Graph Neural Networks versus Self-Attention mechanisms. This paper demonstrates that the future is not about choosing one over the other, but about intelligently integrating them. What makes TriSeRec particularly clever is not just that it uses multiple views, but how it combines them. It utilizes advanced mathematical techniques (Generalized Cauchy-Schwarz divergence) to disentangle "consistent" information (what all views agree on) from "complementary" information (unique insights provided by a specific view). This ensures the model is both robust against noise and sensitive to subtle user signals. This approach sets a new standard for how we design multi-view learning systems in recommendation and beyond.
Xinchen Yuan
Read the Original
This page is a summary of: TriSeRec: A Tri-view Representation Learning Framework for Sequential/Session-based Recommendation, November 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3746252.3761014.
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