Summary
When recommending what a user might want next, looking only at their recent actions can be limiting, especially if those actions don’t give a clear hint. The authors introduce BehaveRec, which combines recent session data, long-term user history over three months, and community behavior patterns to improve recommendations. They find that this approach helps mainly for users with a rich history of past interactions, while for users with few past actions, simpler methods work better. Their results suggest a practical rule: use long-term behavior and community signals when a user has at least about 20 past interactions to get better predictions.
What this means in practice
- •For e-commerce platform engineers: Improve product recommendations by incorporating long-term user activity and community signals for users with sufficient interaction history.
- •For video streaming service teams: Enhance content suggestion accuracy for viewers with frequent history by combining session and community viewing behaviors.
Authors
Aysenur Inan, Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Selene Xu, Kaushiki Nag, Jianpeng Xu, Sushant Kumar
Abstract
Sequential recommendation models typically predict a user's next action from recent session interactions, which limits personalization when short-term signals are weak or ambiguous. Rather than assuming that incorporating longer-term history and community signals is universally beneficial, we study when it actually helps. We propose BehaveRec, a three-signal framework combining a causal session transformer, a bidirectional User History Encoder over a 90-day window, and a GraphSAGE module over a user-user graph built from behavioral embedding similarity rather than item co-occurrence; a learned gate blends the three signals per user. On an industrial dataset of 137M sequences from 12.6M users, BehaveRec improves R@5 by +3.2% over SASRec, with a larger-capacity SASRec baseline (more parameters than BehaveRec) still trailing by +2.5%, ruling out capacity as the explanation. More importantly, a user-level stratification by history length reveals a clear dose-response relationship: BehaveRec's advantage is negligible below roughly 10 interactions and grows monotonically with history richness, reaching +0.017 R@5 for users with 101+ events. This pattern is corroborated across one industrial and three public datasets spanning history-rich to history-sparse regimes, where session-only models remain preferable. These headline results, and the resulting deployment threshold, are established for category-level intent prediction; a preliminary, single-run check on the full item-level vocabulary yields a directionally mixed result and is scoped as an open question rather than a claim. We use these results to derive a practical deployment rule -- roughly 20 interactions per user -- for when long-term history and behavioral-graph signals are worth their added complexity. Despite this added complexity, BehaveRec remains lightweight: it uses 5.67M parameters and trains on a single GPU in under two days.