# Atomic Files¶

Atomic files are introduced to format the input of mainstream recommendation tasks in a flexible way.

So far, our library introduces six atomic file types, and we identify different files by their suffixes.

Suffix

Content

Example Format

.inter

User-item interaction

user_id, item_id, rating, timestamp, review

.user

User feature

user_id, age, gender

.item

Item feature

item_id, category

.kg

Triplets in a knowledge graph

entity, item_id

.net

Social graph data

source, target

Atomic files are combined to support the input of different recommendation tasks.

One can write the suffixes into the config arg load_col to load the corresponding atomic files.

For each recommendation task, we have to provide several mandatory files:

Mandatory atomic files

General

.inter

Context-aware

.inter, .user, .item

Knowledge-aware

Sequential

.inter

Social

.inter, .net

## Format¶

Each atomic file can be viewed as a m x n table, where n is the number of features and m-1 is the number of data records(one line for header).

The first row corresponds to feature names, in which each entry has the form of feat_name:feat_type，indicating the feature name and feature type.

We support four feature types, which can be processed by tensors in batch.

feat_type

Explanations

Examples

token

single discrete feature

user_id, age

token_seq

discrete features sequence

review

float

single continuous feature

rating, timestamp

float_seq

continuous feature sequence

vector

## Examples¶

We present three example data rows in the formatted ML-1M dataset.

ml-1m.inter

user_id:token

item_id:token

rating:float

timestamp:float

1

1193

5

978300760

1

661

3

978302109

ml-1m.user

user_id:token

age:token

gender:token

occupation:token

zip_code:token

1

1

F

10

48067

2

56

M

16

70072

ml-1m.item

item_id:token

movie_title:token_seq

release_year:token

genre:token_seq

1

Toy Story

1995

Animation Children’s Comedy

2

Jumanji

1995

ml-1m.kg

relation_id:token

tail_id:token

m.0gs6m

film.film_genre.films_in_this_genre

m.01b195

m.052_dz

film.film.actor

m.02nrdp

item_id:token

entity_id:token

2694

m.02hxhz

2079

m.0kvcr9

For users who want to load features from additional atomic files (e.g. pretrained entity embeddings), we provide a simple way as following.

Firstly, prepare your additional atomic file (e.g. ml-1m.ent).

ent_id:token

ent_emb:float_seq

m.0gs6m

-115.08 13.60 113.69

m.01b195

-130.97 263.05 -129.88

Secondly, update the args as:

additional_feat_suffix: [ent]
# inter/user/item/...: As usual
ent: [ent_id, ent_emb]


Then, this additional atomic file will be loaded into the Dataset object. These new features can be used as following.

dataset = create_dataset(config)
print(dataset.ent_feat)


Note that these features can be preprocessed by the same way as the other features.

For example, if you want to map the tokens of ent_id into the same space of entity_id, then update the args as:

additional_feat_suffix: [ent]