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Movie Recommender System Based
on Collaborative Filtering Using
Apache Spark
Mohammed Fadhel Aljunid and D. H. Manjaiah
Abstract Recently, the building of recommender systems becomes a significant
research area that attractive several scientists and researchers across the world. The
recommender systems are used in a variety of areas including music, movies,
books, news, search queries, and commercial products. Collaborative Filtering
algorithm is one of the popular successful techniques of RS, which aims to find
users closely similar to the active one in order to recommend items. Collaborative
filtering (CF) with alternating least squares (ALS) algorithm is the most imperative
techniques which are used for building a movie recommendation engine. The ALS
algorithm is one of the models of matrix factorization related CF which is con-
sidered as the values in the item list of user matrix. As there is a need to perform
analysis on the ALS algorithm by selecting different parameters which can even-
tually help in building efficient movie recommender engine. In this paper, we
propose a movie recommender system based on ALS using Apache Spark. This
research focuses on the selection of parameters of ALS algorithms that can affect
the performance of a building robust RS. From the results, a conclusion is drawn
according to the selection of parameters of ALS algorithms which can affect the
performance of building of a movie recommender engine. The model evaluation is
done using different metrics such as execution time, root mean squared error
(RMSE) of rating prediction, and rank in which the best model was trained. Two
best cases are chosen based on best parameters selection from experimental results
which can lead to building good prediction rating for a movie recommender.
Keywords Recommender systems Collaborative filtering Alternating Least
Squares Apache Spark Big data MovieLens dataset
M. F. Aljunid (&)
Mangalore University, Mangalore, Karnataka, India
e-mail: Ngm505@yahoo.com
D. H. Manjaiah (&)
Department of Computer Science, Mangalore University, Mangalore
Karnataka, India
e-mail: drmdh2014@gmail.com
©Springer Nature Singapore Pte Ltd. 2019
V. E. Balas et al. (eds.), Data Management, Analytics and Innovation,
Advances in Intelligent Systems and Computing 839,
https://doi.org/10.1007/978-981-13-1274-8_22
283
1 Introduction
In recent times, big data is becoming one of the newest research interests in the
areas of computer science and other related areas. With the possibility of a radical
change in companies and organizations that use the information for improving the
customer experience and transform their business models. Big data has several
features which are volume, velocity, variety, value, and veracity. Big data is facing
difficulties in managing using conventional tools, techniques, and procedures. Big
data analytics is used for handling bulk quantities of data. It is used to mine and
extract patterns, information, and knowledge from the data in an effective way. Big
data analytics become an important trend for organizations and enterprises that are
interesting in providing innovative ideas for enhancing and increasing their busi-
ness performance and decision-making. RS are a group of techniques that allow
filtering through large samples and information space in order to give suggestion to
users when needed. Currently, RS are becoming highly popular and utilized in
different areas such as movies, research articles, search queries, news, books, social
tags, and music. Furthermore, there are other essential RS basically applicable for
specialist, collaborators, funny story, restaurant and hotels, dresses, monetary ser-
vices, life insurance, passion associates which give online dating services and
several other social media such as Twitter, LinkedIn, and Facebook.
RS use a number of different technologies to filter out best suit results and
provide to users to satisfy their information need. RS are classified into three broad
groups which are content-based systems, collaborative filtering systems, and hybrid
recommender system [1]. Content-based systems which try to test the behavior of
the item which is labeled as recommended one. It works by learning the behavior of
the new users based on their information need presented in objects whereby the user
has rated. It is a keyword-specific RS where the keywords are used to illustrate the
items. Thus, in a content-based RS, models work in such a way that they recom-
mend users’comparable items that have been liked in the past or is browsing
currently. For instance, if a MovieLen user has to browse several comedies movies,
then, the RS will classify those movies into the database as getting the most ratings
on the comedy varieties. Collaborative filtering system is based on similarity
measures between user’s information need and the items. The items recommended
to a new user are those which were liked by other similar users in previous
browsing history. Collaborative filtering algorithm uses an average rating of
objects, recognizes similarities between the users on the basis of their ratings, and
generates new recommendations based on inter-user comparisons. However, it
faces many challenges and limitation such as data sparsity whose role is to the
evaluation of large item set. Another limitation is hard to make prediction based on
nearest neighbor algorithm, third is scalability in which number of users and
number of items both increases, and the last one is cold start where poor rela-
tionship among like-minded people. To solve encounters, above mentioned, we
moved to other approaches of collaborative filtering, and we landed up on
model-based collaborative filtering [2]. Hybrid RS performs their tasks by
284 M. F. Aljunid and D. H. Manjaiah
considering the combining behavior of content-based and collaborative filtering
techniques in such a way that it suits a particular item. Hybrid recommended system
is regarded as the most frequently used RS system considered by many companies
due to its ability to eliminate any weakness that might have arose when one RS is
employed and in addition, its strength is the composite of more than two RS.
The main focus of this work is collaborative filtering system. It is well known that
collaborative filtering could be described as a procedure whereby autom atic prediction
(i.e., filtering) about the interests of a user is made by gathering taste or preferences
information from many users. The unexpressed assumption of the collaborative fil-
tering approach can be best explained, viz., supposing a person A has similar opinion
with person B on a particular issue, the assumption is that person A will be more likely
to have the same opinion as person B on a different issue X did the opinion on X of a
person chosen randomly [3]. Take for an instance the movie “RS”depicted in Fig. 1
which started with a matrix whose entries are movies rated by users. Both user (shown
in green) and a particular movie (shown in blue) are represented each by column and
rows respectively. Owing to the fact that not all users have rated all movies, all the
entries in the matrix are unknown, which necessitate the need for collaborative fil-
tering. There are ratings for only a subset of the movies for each user. With collabo-
rative filtering, the idea is to approximate the rating matrix by factorizing it as the
product of two matrices. That is the one that describes properties of each user (shown in
green), and the other describing properties of each movie.
The minimization of the error for the users/movies pairs was chosen as the basis
for the selection of the two matrices. The alternating least squares algorithm
(ALS) which achieves this by randomly filling the user’s matrix with values before
optimizing the value of the movies was used for this purpose. The value of the
user’s matrix is optimized with the movie’s matrix being kept constant (Fig. 1).
Owing to a fixed set of user factors (i.e., values in the user’s matrix), known ratings
are employed to find the best values by optimizing the movie factors, written on top
of the figure. The best user factor with the fixed movie factors is sleeted. This paper,
reports for the first time, a movie recommendation system based on collaborative
filtering using apache spark. The performance analysis and evaluation of proposed
approach are performed on a MovieLens dataset. From the results obtained, it is
concluded that the selection of parameters of ALS algorithms can affect the per-
formance of recommender engine to be used.
Fig. 1 Low rank factorization matrix [3]
Movie Recommender System Based on Collaborative Filtering …285
The remainder of this paper is organized as follows: related work is provided in
Sect. 2. Section 3introduces the proposed movie recommender system using col-
laborative filtering with ALS algorithm while the experimental study is introduced
in Sect. 4. Finally, the paper conclusion is presented in Sect. 5.
2 Related Work
So far, several researchers introduced and presented research in the area of building
recommendation systems. Wei et al. [4] proposed a hybrid recommender model to
address the cold start problem, which explores the item content features, learned
from a deep learning neural network and applies them to the timeSVD++ CF model.
A hybrid recommendation model is proposed which combines a time-aware model
timeSVD++ with a deep learning architecture SDAE to address the cold start
problem of collaborative filtering recommendation models. Kupisz and Unold [5]
developed and compared item-based collaborative filtering algorithm using two
cluster computing frameworks normally Hadoop’s disk-based MapReduce para-
digm and Spark’s in-memory based RDD paradigm. In order to enhance the reli-
ability, scalability, and to improve processing ability of large-scale data, Zeng et al.
[6] proposed PLGM. In their work, two matrix factorization algorithms were
considered, which are ALS and SGD. The parallel matrix factorization based on
SGD was implemented on spark and was compared with ALS in MLib for its
performance. The advantage and disadvantage of each model based on test results
were analyzed. A variety of profile aggregation approaches were studied and the
model which gives the best result was adopted. Models such as PLGM and LGM
were studied in terms of efficiency and accuracy. Dianping, Lakshmi et al. [7] used
item-based collaborative filtering techniques. In this method, they first inspect the
user item rating matrix and they categorize the relationships among different items,
and they utilize these relationships so as to figure out the recommendations for the
user. A new concept namely movie swarm mining was proposed by Halder et al. [8]
using format frequent item mining and two pruning rules. It addresses the problem
of item recommendation and thus gives an idea about the user interests and famous
movies trend. This technique can be very helpful for movie producers to manage
their new movies. In addition to this, a new algorithm was proposed to recommend
movies to a new user. A scalable method for building recommender systems based
on similarity join has been proposed by Dev et al. [9]. MapReduce framework was
used to design the system in order to work with big data applications. The
unnecessary computation overhead such as redundant comparisons in the similarity
computing phase can significantly be reduced by the system using a method called
extended prefixfiltering (REF). Chen et al. [10] used co-clustering with augmented
matrices (CCAM) to design several methods including a heuristic scoring, tradi-
tional classifier, and machine learning to build a recommendation system and
integrate content-based collaborative filtering for a hybrid recommendation system.
Similarly, a collaborative filtering algorithm based on the ALS, as a powerful
286 M. F. Aljunid and D. H. Manjaiah
matrix decomposition algorithm, has been proposed by Wilkinson and Schreiber
[11]. They found out that it can be awesome to extend to the distributed computing
and solve the data sparse problem.
3 Proposed Movie Recommender System
This section provides the idea of the proposed system. The proposed system is a
movie recommender system based on ALS using Apache Spark. The novelty of this
work is based on the selection of parameters of ALS algorithms that can affect the
performance of building of a movie recommender system.
3.1 Proposed System Block Diagram
In this work, we apply user’s ratings from the datasets the popular website like
IMDB, Rotten Tomatoes, MovieLen, and Time Movie Ratings. This dataset is
available in many formats such as CSV file, text file, and databases. We can either
stream the data live from the websites or download and store them on our local file
system or HDFS. Spark streaming is used to stream real-time data from the various
source like Twitter, the stock market, and geographical system and perform pow-
erful analytics to businesses. It used for processing real-time streaming data. We use
collaborative filtering (CF) to predict the ratings of users for particular movies
based on their ratings for other movies. Then collaborate this with another user’s
rating for that particular movie. We train the ALS algorithm using MovieLen data
and get the results from the machine learning model. We use spark SQL’s data
frame, dataset, and SQL service to store the data. The result of the machine learning
model is stored in RDBMS so that the web application can display the recom-
mendation to a particular use. The results of the movie recommendation system are
stored in our local drive. We store the recommendation movies along with the
ratings in a text file and CSV file formats. We prefer storing the result into an
RDBMS system so as to access it directly from the web application and display
recommendation and top movies as shown in Fig. 2.
3.2 Proposed System Steps
This subsection provides the steps of applying the ALS algorithm on MovieLens
datasets for train and test the selection of best parameter when building a movie
recommendation system.
Movie Recommender System Based on Collaborative Filtering …287
Movie Recommendation System using CF with ALS
Input: MovieLens Dataset
Output: Top Recommended Movies.
Procedures:
Procedure 1:Parsing and loading datasets
Procedure 2: Recognize the user as new or regular.
If new user goto Procedure 5
Procedure 3: Load training and test data into the table (userId, movieId, rating)
def parse_the_rating(line):
x = line.split()
return (int (x [0]), int (x [1]), float (x [2]))
training = sc.TrainingFile("__").map(parse_the_Rating).cache()
test = sc.Testfile(“__”).map(parse_the_Rating)
Procedure 4: Train the recommender model.
New_model= ALS.train (rank, train, iteration)
Procedure 5:Create predictions on (user, movie) pairs from the test data
Predict = New_model.predictAll (test.map(lambda x: (x[0], x[1]))
Procedure6: Adding new user ratings
Procedure 7: Display top N recommended movies.
Procedure 8: Save the New_model
4 Experimental Study
This section presents the experimental setup and results in discussion and analysis.
4.1 Apache Spark
Apache Spark [12] is a rapid and general-purpose cluster computing system. It
introduces high-level application programming interfaces (APIs) using
Fig. 2 Proposed movie recommendation system using CF with ALS
288 M. F. Aljunid and D. H. Manjaiah
programming languages such as Java, Python, Scala, and R, and has an engine that
supports general execution graphs. It also supports a good set of higher level tools
involving Spark SQL for structured data processing, MLlib for machine learning,
GraphX for graph processing, and Spark Streaming for real-time applications. It
was built on top of Hadoop and MapReduce and extends the MapReduce Model to
efficiently use more types of computations. Spark application runs as a separate set
of process on the cluster. All of the distributed processes are coordinated by a
SparkContext object in the drive program. SparkContext connects to one type of
cluster manager (standalone/Yarn/Mesos) for resource allocation across clusters.
Cluster manager provides executors, which are essentially JVM process to run the
logic and store application data. Then the SparkContext object sends the application
code (jar files/python scripts) to executors. Finally, the SparkContext executes tasks
in each executor.
4.2 Data Preprocessing
The dataset which is used in this work is MovieLens dataset. This dataset contains
24 million ratings and 670,000 tag applications applied to 40,000 movies by
260,000 users. This dataset contains three files called ratings.csv, movies.csv and
tags.csv. ratings.csv contains tree column (userId, movieId, rating). While movies.
csv contains movieId, title, genres. The genres have the format: Genre1, Genre2,
Genre3. The tags file (tags.csv) has the format: userId, movieId, tag, timestamp and
finally, the links.csv file has the format: movieId, imdbId, tmdbId. We can split the
data into three portions which are training, validation, and test data to parse their
lines once they are loaded into RDDs. Parsing the movies and rating files yields two
RDDs: For each row in the ratings dataset, we have created a vector of (userId,
movieId, rating). During preprocessing, we have dropped the timestamp attribute
because we do not need it for this recommender. Similarly, each row in the movies
dataset, we have created a vector of (movieId, title). We have dropped the genres
attribute because we do not use it for this recommender.
In order to determine the best ALS parameters for our experiments, we need to
break up the ratings RDD dataset into three pieces as follows: a training set which
we will use 60% of the data to train models, validation set, which used 20% of the
data to choose the best model and test set, which used 20% of the data for our
experiments to randomly split the dataset into the multiple groups.
4.3 Experimental Environment
The test has been done on a machine which contains the subsequent descriptions
P. A machine with Ubuntu 14.04 LTS, 4 GB memory, and Intel® Core™i5-2400
CPU @ 3.10 GHz 4 processor as well as a hard disk of 500 GB. In this machine,
Movie Recommender System Based on Collaborative Filtering …289
Apache Spark with version 2.1.1 is installed and is used to develop the proposed
system. The dataset which is used in research work is MovieLens dataset [13]. In
the proposed model, root mean squared error (RMSE) is used as a performance
measure. RMSE works by measuring the difference between error rate a user gives
to the system and the predicted error by the model. Equation (1) depicts how RMSE
works on movie recommender system.
RMES ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
n
i¼0
xui ygi
2
n
v
u
u
tð1Þ
whereby x
ui
is the rating that user ugives to an item iin the experimental data, y
gi
is
a predicted rating that the movie that user ugives to an item and where nis the
number of ratings in the test data.
4.4 Experimental Results Analysis and Discussion
Recommender system (RS) is becoming growingly popular. In this work, Apache
Spark is used to demonstrate an efficient parallel implementation of a collaborative
filtering method using ALS. ALS is used for dimensionality reduction purpose
which helps in overcoming the limitations of collaborative filtering such as data
sparsity and scalability. The challenges of data sparsity are appearing in numerous
situations, specifically, another problem, when a new an item or user has just added
to the system, it is difficult to find similar ones since there is no sufficient infor-
mation, this problem is called cold start problem [14,15]. When selecting the ALS
algorithm as a part of building the proposed movie recommender system, there is
basic parameter through them can determine the best rating of users for given
movies. These parameters are Rank, Iterations, and Lambda.
The contribution of this paper is to study and determine the selection of
parameters that affect the performance of ALS model in building a movie recom-
mender system because from literature study, it is found that little research work
focused on the study of the selection of ALS’s parameters that can affect its per-
formance in building a movie recommender engine using Apache Spark. The
parameters, lambda, and iterations are used in order to control and adjust the
predicting capability of matrix factorization which is depending on ALS technique
which in turn affect the evaluation of movie RS. The iterations and lambda
parameters are used as follows: Lambda which specifies the regularization
parameter in ALS and iterations in which the proposed model should run the
specified number of iterations. The ALS algorithm achieves its optimal solution
between 5 and 20 iterations.
The parameters lambda and iteration in ALS model are used with different
thresholds to realize the effects of matrix factorization performance on the perfor-
mance of recommendation results and thus take the most appropriate parameters for
290 M. F. Aljunid and D. H. Manjaiah
the following test setups. Tables 1,2and 3show the performance of movie rec-
ommendation engine based on ALS under different values of lambda and iteration.
Table 1illustrates the execution of time with the changes of lambda with iterations
parameters of ALS model, while Table 2the rank of best-trained model with the
changes of lambda with iterations parameters of ALS algorithm, finally Table 3
indicates the RMSE with the changes of lambda with iterations parameters of ALS
model. The results presented in Table 1indicate that when lambda is set to 0.6 and
iteration set is 10, the time value is minimum which is 1.41323 s, and rank value is
8as shown in Table 2. Moreover, the RMSE register for this rating is 1.07424
as indicated in Table 3. On the other hand, as it is indicated in Table 1when
lambda is set to 0.2 and iteration is 15, running time becomes 1.463743 and rank is
12 for this item as shown in Table 2. The RMSE value for this item is the mini-
mum, which is 0.9167, as presented in Table 3.
As mentioned above, the analysis for movie recommendation system is done
using three quality metrics which are RMSE, time, and rank. Using these three
metrics, two cases are achieved as shown in Table 4, case 1 with high time and low
RMSE rate while the case 2 with low time and high RMSE rate. According to
results in Table 4, the prediction for Top 25 movies is shown in Figs. 3and 4.
Table 1 Time of matrix
factorization using lambda
and iteration parameters
Lambda Iteration
5 1015 2025
0.1 1.489 1.454 1.473 1.469 1.512
0.2 1.485 1.437 1.464 1.438 1.514
0.3 1.472 1.481 1.4671 1.441 1.494
0.4 1.658 1.486 1.476 1.495 1.473
0.5 1.431 1.492 1.468 1.478 1.528
0.6 1.615 1.413 1.442 1.459 1.480
0.7 1.443 1.475 1.471 1.446 1.543
0.8 1.554 1.470 1.459 1.449 1.527
0.9 1.491 1.478 1.482 1.471 1.446
Table 2 Rank of matrix
factorization using lambda
and iteration parameters
Lambda Iteration
5 10152025
0.1 4 12 4 4 4
0.2 8 12 12 12 12
0.3 48888
0.4 48888
0.5 88888
0.6 8 88812
0.7 12 12 4 4 12
0.8 12 12 4 4 4
0.9 12 4 4 4 4
Movie Recommender System Based on Collaborative Filtering …291
Table 3 RMSE of matrix factorization using lambda and iteration parameters
Lambda Iteration
5 1015 2025
0.1 0.947 0.942 0.940 0.938 0.938
0.2 0.919 0.917 0.9167 0.917 0.917
0.3 0.941 0.941 0.941 0.941 0.941
0.4 0.975 0.980 0.980 0.981 0.981
0.5 1.018 1.024 1.024 1.024 1.024
0.6 1.069 1.074 1.074 1.074 1.074
0.7 1.127 1.130 1.131 1.131 1.131
0.8 1.192 1.193 1.193 1.193 1.193
0.9 1.261 1.261 1.261 1.261 1.261
Table 4 Two cases for selecting parameters for ALS
Metrics Case
Case 1 Case 2
Time 1.41323 1.463743
Rank 8 12
RMSE 1.07422 0.9167
Fig. 3 Prediction of top 25 movies for case 1
292 M. F. Aljunid and D. H. Manjaiah
In general, the lowest value of the RMSE is considered the best case for pre-
diction in building recommendation system. Therefore, we will adopt the second
case because the value of the RMSE is smaller compared to the value in the first
case as well as adopt the second case as the best case because there is no significant
difference in the amount of time execution between the two cases. Now, we can get
the top recommended movies by using the second case. Finally, we concluded that
from these results the best case is the second case which has the best value for
RMSE, which can be useful for building recommendation engines for predicting the
top 25 ranked movies.
5 Conclusion and Future Work
Movie recommender system plays a significant role in identifying a set of movies
for users based on user interest. Although many move recommendation systems are
available for users, these systems have the limitation of not recommending the
movie efficiently to the existing users. This paper presented a movie recommender
system based on collaborative filtering using Apache Spark. From the results, the
selection of parameters of ALS algorithms can affect the performance of building of
a movie recommender engine. System evaluation is done using various metrics
such as execution time, RMSE of rating prediction, and rank in which the best
Fig. 4 Prediction of top 25 movies for case 2
Movie Recommender System Based on Collaborative Filtering …293
model was trained. Two best cases are chosen based on best parameters selection
from experimental results which can lead to building god prediction rating for a
movie recommender engine. From these cases, the lowest value of the RMSE is
considered the best case for prediction in building movie recommendation system.
Therefore, the second case is recommended to be used since the value of the RMSE
is smaller compared to the value in the first case as well as adopt the second case as
the best case, because there is no significant difference in the amount of time
execution between the two cases. Finally, we concluded that from these results that
the best case is the second case which has the best value for RMSE, which can be
useful for building recommendation engines for predicting the top 25 ranked
movies. In the future work, we plan to develop and improve a new loss function
because of the shortcomings of the recommender system algorithm based on ALS
model based on the parameter of the best case which has the best value for RMSE
using Apache Spark.
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