Building a Simple Similar Movies Recommender System

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Why?

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I recently came across a movie/tv-show recommender, couchmoney.tv. I loved it. I decided that I wanted to build something similar, so I could tinker with it as much as I wanted.

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I also wanted a recommendation system I could use via a REST API. Although I have not included that part in this post, I did eventually create it.

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How?

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By measuring the cosine of the angle between two vectors, you can get a value in the range [0,1] with 0 meaning no similarity. Now, if we find a way to represent information about movies as a vector, we can use cosine similarity as a metric to find similar movies.

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As we are recommending just based on the content of the movies, this is called a content based recommendation system.

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Data Collection

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Trakt exposes a nice API to search for movies/tv-shows. To access the API, you first need to get an API key (the Trakt ID you get when you create a new application).

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I decided to use SQL-Alchemy with a SQLite backend just to make my life easier if I decided on switching to Postgres anytime I felt like.

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First, I needed to check the total number of records in Trakt’s database.

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import requests
+import os
+
+trakt_id = os.getenv("TRAKT_ID")
+
+api_base = "https://api.trakt.tv"
+
+headers = {
+    "Content-Type": "application/json",
+    "trakt-api-version": "2",
+    "trakt-api-key": trakt_id
+}
+
+params = {
+    "query": "",
+    "years": "1900-2021",
+    "page": "1",
+    "extended": "full",
+    "languages": "en"
+}
+
+res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+total_items = res.headers["x-pagination-item-count"]
+print(f"There are {total_items} movies")
+

+ +

There are 333946 movies
+

+ +

First, I needed to declare the database schema in (database.py):

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import sqlalchemy
+from sqlalchemy import create_engine
+from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey, PickleType
+from sqlalchemy import insert
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.exc import IntegrityError
+
+meta = MetaData()
+
+movies_table = Table(
+    "movies",
+    meta,
+    Column("trakt_id", Integer, primary_key=True, autoincrement=False),
+    Column("title", String),
+    Column("overview", String),
+    Column("genres", String),
+    Column("year", Integer),
+    Column("released", String),
+    Column("runtime", Integer),
+    Column("country", String),
+    Column("language", String),
+    Column("rating", Integer),
+    Column("votes", Integer),
+    Column("comment_count", Integer),
+    Column("tagline", String),
+    Column("embeddings", PickleType)
+
+)
+
+# Helper function to connect to the db
+def init_db_stuff(database_url: str):
+    engine = create_engine(database_url)
+    meta.create_all(engine)
+    Session = sessionmaker(bind=engine)
+    return engine, Session
+

+ +

In the end, I could have dropped the embeddings field from the table schema as I never got around to using it.

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Scripting Time

+ +

from database import *
+from tqdm import tqdm
+import requests
+import os
+
+trakt_id = os.getenv("TRAKT_ID")
+
+max_requests = 5000 # How many requests I wanted to wrap everything up in
+req_count = 0 # A counter for how many requests I have made
+
+years = "1900-2021" 
+page = 1 # The initial page number for the search
+extended = "full" # Required to get additional information 
+limit = "10" # No of entires per request -- This will be automatically picked based on max_requests
+languages = "en" # Limit to English
+
+api_base = "https://api.trakt.tv"
+database_url = "sqlite:///jlm.db"
+
+headers = {
+    "Content-Type": "application/json",
+    "trakt-api-version": "2",
+    "trakt-api-key": trakt_id
+}
+
+params = {
+    "query": "",
+    "years": years,
+    "page": page,
+    "extended": extended,
+    "limit": limit,
+    "languages": languages
+}
+
+# Helper function to get desirable values from the response
+def create_movie_dict(movie: dict):
+    m = movie["movie"]
+    movie_dict = {
+        "title": m["title"],
+        "overview": m["overview"],
+        "genres": m["genres"],
+        "language": m["language"],
+        "year": int(m["year"]),
+        "trakt_id": m["ids"]["trakt"],
+        "released": m["released"],
+        "runtime": int(m["runtime"]),
+        "country": m["country"],
+        "rating": int(m["rating"]),
+        "votes": int(m["votes"]),
+        "comment_count": int(m["comment_count"]),
+        "tagline": m["tagline"]
+    }
+    return movie_dict
+
+# Get total number of items
+params["limit"] = 1
+res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+total_items = res.headers["x-pagination-item-count"]
+
+engine, Session = init_db_stuff(database_url)
+
+
+for page in tqdm(range(1,max_requests+1)):
+    params["page"] = page
+    params["limit"] = int(int(total_items)/max_requests)
+    movies = []
+    res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+
+    if res.status_code == 500:
+        break
+    elif res.status_code == 200:
+        None
+    else:
+        print(f"OwO Code {res.status_code}")
+
+    for movie in res.json():
+        movies.append(create_movie_dict(movie))
+
+    with engine.connect() as conn:
+        for movie in movies:
+            with conn.begin() as trans:
+                stmt = insert(movies_table).values(
+                    trakt_id=movie["trakt_id"], title=movie["title"], genres=" ".join(movie["genres"]),
+                    language=movie["language"], year=movie["year"], released=movie["released"],
+                    runtime=movie["runtime"], country=movie["country"], overview=movie["overview"],
+                    rating=movie["rating"], votes=movie["votes"], comment_count=movie["comment_count"],
+                    tagline=movie["tagline"])
+                try:
+                    result = conn.execute(stmt)
+                    trans.commit()
+                except IntegrityError:
+                    trans.rollback()
+    req_count += 1
+

+ +

(Note: I was well within the rate-limit so I did not have to slow down or implement any other measures)

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Running this script took me approximately 3 hours, and resulted in an SQLite database of 141.5 MB

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Embeddings!

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I did not want to put my poor Mac through the estimated 23 hours it would have taken to embed the sentences. I decided to use Google Colab instead.

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Because of the small size of the database file, I was able to just upload the file.

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For the encoding model, I decided to use the pretrained paraphrase-multilingual-MiniLM-L12-v2 model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt.

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While deciding how I was going to process the embeddings, I came across multiple solutions:

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Milvus - An open-source vector database with similar search functionality
FAISS - A library for efficient similarity search
Pinecone - A fully managed vector database with similar search functionality

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I did not want to waste time setting up the first two, so I decided to go with Pinecone which offers 1M 768-dim vectors for free with no credit card required (Our embeddings are 384-dim dense).

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Getting started with Pinecone was as easy as:

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Signing up
Specifying the index name and vector dimensions along with the similarity search metric (Cosine Similarity for our use case)
Getting the API key
Installing the Python module (pinecone-client)

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import pandas as pd
+import pinecone
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm 
+
+database_url = "sqlite:///jlm.db"
+PINECONE_KEY = "not-this-at-all"
+batch_size = 32
+
+pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
+index = pinecone.Index("movies")
+
+model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device="cuda")
+engine, Session = init_db_stuff(database_url)
+
+df = pd.read_sql("Select * from movies", engine)
+df["combined_text"] = df["title"] + ": " + df["overview"].fillna('') + " -  " + df["tagline"].fillna('') + " Genres:-  " + df["genres"].fillna('')
+
+# Creating the embedding and inserting it into the database
+for x in tqdm(range(0,len(df),batch_size)):
+    to_send = []
+    trakt_ids = df["trakt_id"][x:x+batch_size].tolist()
+    sentences = df["combined_text"][x:x+batch_size].tolist()
+    embeddings = model.encode(sentences)
+    for idx, value in enumerate(trakt_ids):
+        to_send.append(
+            (
+                str(value), embeddings[idx].tolist()
+            ))
+    index.upsert(to_send)
+

+ +

That's it!

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Interacting with Vectors

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We use the trakt_id for the movie as the ID for the vectors and upsert it into the index.

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To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.

+ +

def get_trakt_id(df, title: str):
+  rec = df[df["title"].str.lower()==movie_name.lower()]
+  if len(rec.trakt_id.values.tolist()) > 1:
+    print(f"multiple values found... {len(rec.trakt_id.values)}")
+    for x in range(len(rec)):
+      print(f"[{x}] {rec['title'].tolist()[x]} ({rec['year'].tolist()[x]}) - {rec['overview'].tolist()}")
+      print("===")
+      z = int(input("Choose No: "))
+      return rec.trakt_id.values[z]
+  return rec.trakt_id.values[0]
+
+def get_vector_value(trakt_id: int):
+  fetch_response = index.fetch(ids=[str(trakt_id)])
+  return fetch_response["vectors"][str(trakt_id)]["values"]
+
+def query_vectors(vector: list, top_k: int = 20, include_values: bool = False, include_metada: bool = True):
+  query_response = index.query(
+      queries=[
+          (vector),
+      ],
+      top_k=top_k,
+      include_values=include_values,
+      include_metadata=include_metada
+  )
+  return query_response
+
+def query2ids(query_response):
+  trakt_ids = []
+  for match in query_response["results"][0]["matches"]:
+    trakt_ids.append(int(match["id"]))
+  return trakt_ids
+
+def get_deets_by_trakt_id(df, trakt_id: int):
+  df = df[df["trakt_id"]==trakt_id]
+  return {
+      "title": df.title.values[0],
+      "overview": df.overview.values[0],
+      "runtime": df.runtime.values[0],
+      "year": df.year.values[0]
+  }
+

+ +

Testing it Out

+ +

movie_name = "Now You See Me"
+
+movie_trakt_id = get_trakt_id(df, movie_name)
+print(movie_trakt_id)
+movie_vector = get_vector_value(movie_trakt_id)
+movie_queries = query_vectors(movie_vector)
+movie_ids = query2ids(movie_queries)
+print(movie_ids)
+
+for trakt_id in movie_ids:
+  deets = get_deets_by_trakt_id(df, trakt_id)
+  print(f"{deets['title']} ({deets['year']}): {deets['overview']}")
+

+ +

Output:

+ +

[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
+Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
+Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
+The Chase (2017): Some FBI agents hunt down a criminal
+Surveillance (2008): An FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.
+Marauders (2016): An untraceable group of elite bank robbers is chased by a suicidal FBI agent who uncovers a deeper purpose behind the robbery-homicides.
+Miracles for Sale (1939): A maker of illusions for magicians protects an ingenue likely to be murdered.
+Deceptors (2005): A Ghostbusters knock-off where a group of con-artists create bogus monsters to scare up some cash. They run for their lives when real spooks attack.
+The Outfit (1993): A renegade FBI agent sparks an explosive mob war between gangster crime lords Legs Diamond and Dutch Schultz.
+Bank Alarm (1937): A federal agent learns the gangsters he's been investigating have kidnapped his sister.
+The Courier (2012): A shady FBI agent recruits a courier to deliver a mysterious package to a vengeful master criminal who has recently resurfaced with a diabolical plan.
+After the Sunset (2004): An FBI agent is suspicious of two master thieves, quietly enjoying their retirement near what may - or may not - be the biggest score of their careers.
+Down Three Dark Streets (1954): An FBI Agent takes on the three unrelated cases of a dead agent to track down his killer.
+The Executioner (1970): A British intelligence agent must track down a fellow spy suspected of being a double agent.
+Ace of Cactus Range (1924): A Secret Service agent goes undercover to unmask the leader of a gang of diamond thieves.
+Firepower (1979): A mercenary is hired by the FBI to track down a powerful recluse criminal, a woman is also trying to track him down for her own personal vendetta.
+Heroes & Villains (2018): an FBI agent chases a thug to great tunes
+Federal Fugitives (1941): A government agent goes undercover in order to apprehend a saboteur who caused a plane crash.
+Hell on Earth (2012): An FBI Agent on the trail of a group of drug traffickers learns that their corruption runs deeper than she ever imagined, and finds herself in a supernatural - and deadly - situation.
+Spies (2015): A secret agent must perform a heist without time on his side
+

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For now, I am happy with the recommendations.

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Simple UI

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The code for the flask app can be found on GitHub: navanchauhan/FlixRec or on my Gitea instance

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I quickly whipped up a simple Flask App to deal with problems of multiple movies sharing the title, and typos in the search query.

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Home Page

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Home Page

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Handling Multiple Movies with Same Title

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Multiple Movies with Same Title

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Results Page

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Results Page

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Includes additional filter options

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Advance Filtering Options

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Test it out at https://flixrec.navan.dev

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Current Limittations

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Does not work well with popular franchises
No Genre Filter

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Future Addons

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Include Cast Data +
- e.g. If it sees a movie with Tom Hanks and Meg Ryan, then it will boost similar movies including them
- e.g. If it sees the movie has been directed my McG, then it will boost similar movies directed by them
REST API
TV Shows
Multilingual database
Filter based on popularity: The data already exists in the indexed database

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