Readme--MIFN

This is the our implementation for the paper: Exploring Mixed Information Flow for Cross-domain Sequential Recommendations.

The datasets used in this paper are Movie, Book, Kitchen, Food domains from Amazon data.

1. MIFN.py is the model code.
2. Train.py is the training code;
3. extract_kg.py is the knowledge graph extraction algorithm.

Knowledge construction

As for knowledge construction, we first use the metadata from Amazon http://jmcauley.ucsd.edu/data/amazon/.

There are four relationships among these knowledge triples, which are “also-buy”, “also-view”, “buy-after-viewing” and “buy-together”.

We direct use these triples as part of our TKDD knowledge.

As for “Movie-Book” domain, we find there maybe some adaptation relations between books and movies,

e.g., if one movie is adapted from one book, there exists such an “adaptation” relationship between them.

Therefore, we crawl some triples from Wikipedia as below.

A Crawler for Adaptation

1. list_film_book_en.py for Category:Lists of Films based on books, which saves tabular records.

2. tv_book_en.py for Category:Television shows based on books.

3. film_book_en.py for Category:Films based on books. It is similar with tv.

But note that, in film_book_en.py, if a book name is not found, we use the category instead.

For example, Arabian Nights (2015 film) belongs to Category:Films based on One Thousand and One Nights‎. Then One Thousand and One Nights‎ may be the potential base.

It may raise noise, and you can change codes following the pattern tv_book_en.py to avoid. There's little difference in corresponding codes.

File 1 outputs film_book.txt in 'w'(write) mode, while file 2 and 3 output in 'a'(attach) mode. You can run them in order or change modes.

/data/film_book.txt is a previous output file. Codes are modified recently, so re-run may result in a different version.

Categories

Categories of a film or a book can be extracted after requesting wiki metadata api.

Codes to extract have been written in the above-mentioned .py files, but they have been commented out. In this way, you will crawl adaptation relations and categories at the same time.

get_category() in kg_category.py provides another way to use wikipedia library.

Match for amazon dataset

Movie and Book datasets is published in (Amazon Reviews)[http://jmcauley.ucsd.edu/data/amazon/].

join.py matches movies and books which have adaptation relations.

You can also change codes commented out in match() and ceiling() for the rule of matching and filtering.