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.
Figures & tables
Dataset
∣C∣
Users
Avg hist
Med hist
MovieLens ML-1M
18
6,040
165.6
96.0
CDs & Vinyl
310
23,970
17.2
11.0
Electronics
673
39,991
14.0
11.0
TABLE I: Public benchmark statistics.
Dataset
Model
R@1
R@5
R@10
N@5
N@10
Industrial Dataset
Repeat-Last
0.4548
0.4548
0.4548
0.4548
0.4548
BERT4Rec [ 2 ]
0.4998
0.7032
0.7752
0.6182
0.6234
SASRec [ 1 ]
0.4976
0.6975
0.7757
0.5969
0.6154
BehaveRec V5
0.5010
0.7201
0.7772
0.6211
0.6396
MovieLens ML-1M ‡
Repeat-Last
0.3853
0.5517
0.6575
0.4854
0.5177
BERT4Rec [ 2 ]
0.4162
0.8672
0.9631
0.6611
0.6925
TABLE II: Recommendation performance. Bold = best per dataset. Amazon uses category-level prediction (TLSAN benchmark [ 18 ] ). † Amazon avg history 14–17 events; ‡ MovieLens: single run; five-seed mean and per-seed breakdown in Table IX . See Section V-G for cross-domain analysis.
90-day history
SASRec
V5
Δ R@5
1–5
0.612
0.610
−0.002
6–10
0.638
0.639
+0.001
11–20
0.662
0.666
+0.004
21–50
0.691
0.699
+0.008
51–100
0.716
0.728
+0.012
101+
0.731
0.748
+0.017
TABLE III: User-level history stratification on the industrial test set. R@5 by 90-day history length bin, same domain and vocabulary throughout (controls for dataset-level confounds). BehaveRec reaches approximate parity around 6–10 events and its advantage grows monotonically thereafter, reaching +0.017 R@5 at 101+ events. SASRec is preferable only in the sparsest bin (1–5 events).
Fig. 1 : Dose-response relationship between 90-day history length and BehaveRec’s R@5 advantage over SASRec. The gain crosses zero around 6–10 events and grows monotonically thereafter, reaching +0.017 R@5 in the 101+ bin. Data as in Table III .
Sequence type
SASRec
V5
Δ R@5
N
Continuation (45.5%)
0.7471
0.7523
+0.0052
1,356,496
Shift (54.5%)
0.6561
0.6932
+0.0371
1,625,835
TABLE IV : BehaveRec’s advantage over SASRec, stratified by whether the ground truth continues the last session cluster or shifts to a new one (Section V-D ). Single run, no confidence intervals.
Hist. 1–10
Hist. 11–50
Hist. 51+
N
Session 1–3 events
+0.0051
+0.0182
+0.0251
1,268,267
Session 4+ events
−0.0009
+0.0091
+0.0189
1,714,064
TABLE V : Δ R@5 (V5 − SASRec) jointly stratified by session length and 90-day history length. Single run, no confidence intervals.
Model
R@5
HR@10
NDCG@10
SASRec (session-only)
0.4324
0.8236
0.5914
BehaveRec V4 (UHE + session)
0.4218
0.8346
0.5934
TABLE VI : Item-level (full-vocabulary) evaluation on MovieLens ML-1M, ∣I∣=3,706 . Single run, no confidence intervals; reported as a preliminary, directionally mixed result (see discussion below).
Method
R@5
N@5
Δ R@5 vs. V4
(A) No graph (V4)
0.7163
0.6175
–
(B) Item co-occurrence (LightGCN-style)
0.7188
0.6197
+0.0025
(C) Behavioral embedding (V5, ours)
0.7201
0.6211
+0.0038
TABLE VII: Graph construction ablation. The same BehaveRec architecture is used throughout; only the edge definition differs. Evaluated on a 50-part test set (seed=99). All differences are significant at p<0.05 . Edge definitions are swapped at inference using the same trained backbone; variants are not independently retrained from scratch.
#
Variant
R@5
NDCG@5
Δ R@5
0
Full BehaveRec V5
0.7201
0.6211
–
1
w/o graph (= V4)
0.7163
0.6175
−0.0038
2
w/o UHE (= SASRec)
0.6975
0.5969
−0.0226
3
w/o InfoNCE ( λcon=0 )
0.7124
0.6148
−0.0077
4
w/o ThreeWayGate
0.7127
0.6146
−0.0074
5
w/o weight tying
0.7126
0.6145
−0.0075
TABLE VIII: Ablation study on the industrial dataset. All rows trained under the same protocol as BehaveRec V5 (Section IV-I ).
Model
s=42
s=99
s=123
s=456
s=789
Mean
Std
SASRec
0.8790
0.8770
0.8770
0.8785
0.8778
0.8778
0.0009
BehaveRec V5
0.8825
0.8843
0.8845
0.8838
0.8846
0.8839
0.0009
Δ
+0.0035
+0.0073
+0.0075
+0.0053
+0.0068
+0.0061
0.0017
TABLE IX: MovieLens ML-1M R@5 across five seeds. BehaveRec V5 outperforms SASRec on all five seeds ( +0.0061 mean, std 0.0017 ). Absolute values differ slightly from the single run reported in Table II due to run-to-run variation; the direction and approximate magnitude of the improvement are consistent between the two.
Metric
Lift
Surface
Annualized GMV
+\2.4$ M
Item + post-add-to-cart
post-add-to-cart page views
+55 bps
post-add-to-cart
Third-party add-to-cart
+300 bps
post-add-to-cart
New category adoption (10-day)
∼ 2%
Item + post-add-to-cart
Consumables GMV
+0.9%
Home (iOS)
Sponsored impressions
+0.85% / +0.32%
Home iOS/Android
TABLE X : Left: Production A/B test results (lift vs. existing carousel baselines). Right: Signal attribution across 12 illustrative test customers.
Session len
V4 R@5
V5 R@5
Δ
1 event
0.541
0.552
+0.011
2–3 events
0.612
0.619
+0.007
4–6 events
0.701
0.704
+0.003
7–10 events
0.768
0.769
+0.001
11–20 events
0.812
0.812
+0.000
TABLE XI: R@5 by session length: V5 (behavioral graph) vs. V4 (no graph). Graph contribution is largest for cold sessions and diminishes as session evidence strengthens.