What to Watch?

What to Watch?

English translation by SOTA – AI Community of the Russian original. Organisers who would like this translation removed can email [email protected].

All-Russian School Olympiad in Informatics 2025–2026, Final Stage
“Artificial Intelligence” profile, Tour 2, Moscow, 25 March 2026
Task A

Statement

In an online cinema, a user needs to be recommended one film out of several possible options.

For this, the system selects five candidate films for the user and sets a rule by which one of them must be chosen. We will call such a selection task a query.

Each query is described by the following data:

• user_id: the user identifier;
• query_type: the query type, that is, the selection rule;
• $c_1$, $c_2$, $c_3$, $c_4$, $c_5$: the identifiers of five different candidate films.

Each row of the file queries_A.csv specifies one query: for the given user, one of the five proposed films must be chosen.

For each query, you need to output the item_id of the chosen film.

Input format

Use the files attached to the task:

1. queries_A.csv
2. items_A.csv
3. events_A.csv
4. item_meta_A.json
5. sessions_A.json

Notes

The task comes with the file baseline_A.ipynb, which contains an example of reading the input files, filtering the queries by type for each subtask, and a detailed description of the data.

Output format

You need to output a CSV file with the header

query_id,item_id

For each query_id belonging to the corresponding subtask, the answer must contain exactly one row. There must be no extra rows.

Scoring

The maximum score for each subtask is 20.

A solution is considered correct if, for each query_id belonging to the corresponding subtask, the correct item_id according to the rules of the task is given.

The final score for the task is based on the best submission.

A1. Subtask 1

When solving this subtask, filter the rows of queries_A.csv, keeping only the queries of type favorite_genre, and output answers only for them.

For a favorite_genre query, the genre of each of the five candidates is taken, and then, for this genre, the sum of the weights of all events of the given user on films of the same genre is computed, where

• open = 1
• finish = 2
• like = 3

The candidate for whose genre this sum is the largest is chosen.

In case of a tie:

1. the film with the smaller duration;
2. the film with the smaller item_id.

A2. Subtask 2

When solving this subtask, filter the rows of queries.csv, keeping only the queries of type actor_match, and output answers only for them.

For each actor_match query, the set watched_actors is formed from the actors of the films that the user

1. Either watched: finish;
2. Or marked as liked: like.

Only the actors from the actors field of the file item_meta_A.json are considered.

If the actors field is missing, it should be treated as an empty list.

For a candidate, score equals the number of common elements of the sets:

the set of actors of this candidate; the set watched_actors.

In case of a tie, the film with the smallest item_id is chosen.

A3. Subtask 3

When solving this subtask, filter the rows of queries_A.csv, keeping only the queries of type next_in_session, and output answers only for them.

For the user, consider all adjacent pairs of films $f_1 \to f_2$ extracted from all the path lists in the file sessions_A.json.

If path = $[x_1, x_2, \ldots, x_k]$, then the following pairs are extracted from it:

$$\begin{gathered} x_1 \to x_2 \\ x_2 \to x_3 \\ \vdots \\ x_{k-1} \to x_k \end{gathered}$$

For a candidate $f_2$, its score is the number of such pairs $f_1 \to f_2$ for which the user has ever finished watching the film $f_1$, that is, there is a finish event for it in the file events_A.csv.

Each occurrence of a pair in sessions_A.json is counted separately.

If the user has no suitable pairs, the score of every candidate is taken to be 0.

The candidate with the largest score is chosen.

In case of a tie, the film with the smallest item_id is chosen.