136 6. MULTIMODAL SEQUENTIAL LEARNING
In addition, we also conducted the significance test between our model and the most com-
petitive baseline THACIL. We can see that the advantage of our model is statistically significant
as p-value is 2:81 10
5
on the Dataset III-1 and 4:70 10
6
on the Dataset III-2.
To justify the robustness of our proposed model, we comparatively explored the perfor-
mance of our model and the baselines by varying the number of returned items K. Figure 6.5
shows the results regarding the performance comparison on K.
• Jointly analyzing the performance of the models in Figures 6.5a and d, we found that
increasing the number of returned items K degrades the precision value of the recommen-
dation. But our model ALPINE outperforms others under the same experimental setting,
especially on the Dataset III-1.
• e performance of all these methods over recall and F value rises fast as the number of
returned items K linearly increases. eir curves then gradually ascend to a steady state.
Our method ALPINE consistently and remarkably outputs a higher accuracy as compared
to that of other methods, especially on the Dataset-III-2. is verifies the robustness of
our model.
6.5.4 COMPONENT-WISE EVALUATION OF ALPINE
We studied the variants of our model to further investigate the effectiveness of the uninterested
representation modeling, user-matrix, and temporal interest graph:
• ALPINE_u: We eliminated the uninterested representation modeling part from the
model. Namely, we computed the final click probability by interested representation and
multi-level interest representation.
• ALPINE_m: We eliminated the multi-level interest module. at is, the final click prob-
ability is computed by the user’s interested and uninterested representation.
• ALPINE_um: We only utilized the user’s interested sequence to predict the click proba-
bility, namely we eliminated both the uninterested representation modeling and the multi-
level interest modeling layer.
• ALPINE_umg: We eliminated the graph information from the ALPINE_um model.
We compared these variants on the two datasets, and Table 6.2 summarizes the results
regarding the component-wise comparison. By jointly analyzing Table 6.2, we gained the fol-
lowing insights:
• By jointly analyzing the performance of ALPINE_u on the two datasets, it can be seen that
removing the uninterested representation modeling degrades the recommendation results.
To be more specific, ALPINE_u has dropped by 0.2% on the Dataset III-1 and 1.1%
on the Dataset III-2 in terms of AUC. is verifies the effectiveness of the uninterested
representation modeling.
6.5. EXPERIMENTS 137
10 20 30 40 50 60 70 80 90 100
0.28
0.30
0.32
0.34
0.36
0.38
P@ K
K
ALPINE
THACIL
NCF
ATRank
CNN-R
LSTM-R
BPR
10 20 30 40 50 60 70 80 90 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
R@K
K
ALPINE
THACIL
NCF
ATRank
CNN-R
LSTM-R
BPR
10 20 30 40 50 60 70 80 90 100
0.15
0.20
0.25
0.30
0.35
0.40
F@ K
K
ALPINE
THACIL
NCF
ATRank
CNN-R
LSTM-R
BPR
10 20 30 40 50 60 70 80 90 100
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
P@ K
K
ALPINE
THACIL
NCF
ATRank
CNN-R
LSTM-R
BPR
10 20 30 40 50 60 70 80 90 100
0.0
0.1
0.2
0.3
0.4
0.5
0.6
R@K
K
ALPINE
THACIL
NCF
ATRank
CNN
LSTM
BPR
10 20 30 40 50 60 70 80 90 100
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
F@ K
K
ALPINE
THACIL
NCF
ATRank
CNN-R
LSTM-R
BPR
(a) P@K on Dataset III-1 (b) R@K on Dataset III-1
(c) F@K on Dataset III-1 (d) P@K on Dataset III-2
(e) R@K on Dataset III-2 (f ) F@K on Dataset III-2
Figure 6.5: Recommendation performance vs. the number of returned items K over Datasets
III-1 and III-2.
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