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Time and memory cost of knowledge-based recommendation models

Datasets information:

Dataset #User #Item #Interaction Sparsity #Entity #Relation #Triple
ml-1m 6,040 3,629 836,478 0.9618 79,388 51 385,923
ml-10m 69,864 10,599 8,242,124 0.9889 181,941 51 1,051,385
LFM-1b 64,536 156,343 6,544,312 0.9994 1,751,586 10 3,054,516

Device information

OS:                   Linux
Python Version:       3.8.3
PyTorch Version:      1.7.0
cudatoolkit Version:  10.1
GPU:                  TITAN RTX（24GB）
Machine Specs:        32 CPU machine, 64GB RAM

1) ml-1m dataset:

Time and memory cost on ml-1m dataset:

Method Training Time (sec/epoch) Evaluation Time (sec/epoch) GPU Memory (GB)
CKE 3.76 8.73 1.16
KTUP 3.82 17.68 1.04
RippleNet 9.39 13.13 4.57
KGAT 9.59 8.63 3.52
KGNN-LS 4.78 15.09 1.04
KGCN 2.25 13.71 1.04
MKR 6.25 14.89 1.29
CFKG 1.49 9.76 0.97

Config file of ml-1m dataset:

# dataset config
field_separator: "\t"
seq_separator: " "
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
RATING_FIELD: rating
HEAD_ENTITY_ID_FIELD: head_id
TAIL_ENTITY_ID_FIELD: tail_id
RELATION_ID_FIELD: relation_id
ENTITY_ID_FIELD: entity_id
NEG_PREFIX: neg_
LABEL_FIELD: label
load_col:
    inter: [user_id, item_id, rating]
    kg: [head_id, relation_id, tail_id]
    link: [item_id, entity_id]
lowest_val:
    rating: 3
drop_filter_field: True

# training and evaluation
epochs: 500
train_batch_size: 2048
eval_batch_size: 2048
valid_metric: MRR@10

Other parameters (including model parameters) are default value.

2）ml-10m dataset:

Time and memory cost on ml-10m dataset:

Method Training Time (sec/epoch) Evaluation Time (sec/epoch) GPU Memory (GB)
CKE 8.65 85.53 1.46
KTUP 40.71 507.56 1.43
RippleNet 32.01 152.40 4.71
KGAT 298.22 80.94 22.44
KGNN-LS 15.47 241.57 1.42
KGCN 7.73 244.93 1.42
MKR 61.05 383.29 1.80
CFKG 5.99 140.74 1.35

Config file of ml-10m dataset:

# dataset config
field_separator: "\t"
seq_separator: " "
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
RATING_FIELD: rating
HEAD_ENTITY_ID_FIELD: head_id
TAIL_ENTITY_ID_FIELD: tail_id
RELATION_ID_FIELD: relation_id
ENTITY_ID_FIELD: entity_id
NEG_PREFIX: neg_
LABEL_FIELD: label
load_col:
    inter: [user_id, item_id, rating]
    kg: [head_id, relation_id, tail_id]
    link: [item_id, entity_id]
lowest_val:
    rating: 3
drop_filter_field: True

# training and evaluation
epochs: 500
train_batch_size: 2048
eval_batch_size: 2048
valid_metric: MRR@10

Other parameters (including model parameters) are default value.

3）LFM-1b dataset:

Time and memory cost on LFM-1b dataset:

Method Training Time (sec/epoch) Evaluation Time (sec/epoch) GPU Memory (GB)
CKE 62.99 82.93 4.45
KTUP 91.79 3218.69 4.36
RippleNet 126.26 188.38 6.49
KGAT 626.07 75.70 23.28
KGNN-LS 62.55 1709.10 4.73
KGCN 52.54 1763.03 4.71
MKR 290.01 2341.91 6.96
CFKG 53.35 553.58 4.22

Config file of LFM-1b dataset:

# dataset config
field_separator: "\t"
seq_separator: " "
USER_ID_FIELD: user_id
ITEM_ID_FIELD: tracks_id
RATING_FIELD: rating
HEAD_ENTITY_ID_FIELD: head_id
TAIL_ENTITY_ID_FIELD: tail_id
RELATION_ID_FIELD: relation_id
ENTITY_ID_FIELD: entity_id
NEG_PREFIX: neg_
LABEL_FIELD: label
load_col:
    inter: [user_id, tracks_id, timestamp]
    kg: [head_id, relation_id, tail_id]
    link: [tracks_id, entity_id]
lowest_val:
    timestamp: 1356969600
  
highest_val:
    timestamp: 1362067200
drop_filter_field: True
min_user_inter_num: 2
min_item_inter_num: 15

# training and evaluation
epochs: 500
train_batch_size: 2048
eval_batch_size: 2048
valid_metric: MRR@10

Other parameters (including model parameters) are default value.