The document presents a research on developing a demography-based hybrid recommender system for movies, which integrates collaborative filtering, content-based filtering, and demographic filtering to address the cold start problem. The system processes user demographic data and suggests relevant movie recommendations tailored to user age categories while utilizing similarity measures and various filtering techniques. Limitations and potential future scope for the model include applicability to larger datasets and further enhancement of recommendation strategies.

1. Bebin K Raju
Msc Data Science
Departmentof computer Science
CHRIST (Deemed to be University)
DEMOGRAPHY BASED HYBRID SYSTEM
FOR MOVIE RECOMMENDATIONS
Presented at
ICSAC 2021-International Conference on
Sustainability and Advanced Computing, Springer
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Dr. Ummesalma M
AssistantProfessor
Departmentof computer Science
CHRIST (Deemed to be
University)

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Outline
Introduction
Problem Statement
Objective of Research
Related Works
Necessity of Defining the Problem
Research Design
Implementation with Results
Limitations
Conclusion
Future Scope
References

3. Introduction
 A recommender system, or a recommendation system is an information
filtering system that recommends the related suggestions as per the end
users requirement.
 Applications: movies, music, serials, books, documents, websites, tourism
etc.
• Benefits: RSs are beneficial to both service providers and to the users.
• RSs reduce transaction costs of finding and selecting items.
• RSs help in decision making.
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Problem Statement
To build a Demography Based Hybrid recommender
system for Movie Recommendation.
Objectives of Research
To build a Hybrid Recommender System by combining
collaborative filtering, content-based filtering and
demography based filtering techniques to solve the cold
start problem.

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Related Works
An Improved Hybrid Recommender System by
Combining Predictions [1]
Belkacem and et.al
• Collaborative filtering, content-based and demographic
filtering is used to predict ratings dynamically
• Neighbourhood based collaborative filtering is applied to
which a content recommender with clustered
demographics is taken together to show the
recommendations.

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Related Works cont.
Evaluating the impact of demographic data on a
hybrid recommender model [2]
Edson B. Santos Junior and et.al
•A Multifaceted hybrid recommender model is
evaluated to see how applying demographic details adds
on to the recommender
• Adding demographic details to the recommender adds
additional information and produces good results

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Related Works cont.
A Scalable, Accurate Hybrid Recommender System [3]
Mustansar Ali Ghazanfar and et.al
• A hybrid recommendation approach by combining the rating,
feature and demographic information about items.
• Combining known and existing feature sets can solve the cold
start problem.

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Related Works cont.
Hybrid Recommender Systems: A Systematic Literature
Review [4]
Erion Cano and Maurizio Morison
• Exploring through the literature for different state of the art
Hybrid recommender systems
• Steps to follow to a problem statement, paper collection,
quality assessment, data extraction and Synthesis

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Related Works cont.
Improved Movie Recommendations Based on a
Hybrid Feature Combination Method [5]
Gharbi Alshammari and et.al
• The user-item ratings details are combined and matched before
applying the similarity matrix for prediction
• AdaBoost classifier outperforms all the other techniques in
terms of accuracy and Root Mean Squared error

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Related Works cont.
Surprise: A Python library for recommender systems [6]
Nicolas Hug
• Surprise is a Python library for building and analyzing rating
prediction algorithms
• Surprise provides a collection of estimators (or prediction
algorithms) for rating prediction
•Surprise package can be used by researchers for doing
recommendation system research

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Related Works cont.
A Movie and Book Recommender System using Surprise
Recommendation kit [7]
Ananth G S
• The movie and book recommenders are implemented using the
algorithms in the surprise library
• The Root mean squared error is used to evaluate the models

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Related Works cont.
Collaborativ Filtering vs Content-Based Filtering:
differences and similarities [8]
Rafael Glauber and et.al
• Content based approach with bag of words
and term frequencyinverse document frequency before applying
interactions similarity measure
• Different similarity measures can be used to compare and
produces good recommendations

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Related Works cont.
The MovieLens Datasets: History and Context [9]
F. Maxwell Harper and Joseph A. Konstan, University of
Minnesota
• A good overview of all the attributes used in the
movielens dataset and its best practices
• This gives a very good understanding about the dataset,
its attributes, how the data was collected, limitations and
Alternatives which aided in the research study

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Necessity of Defining the Problem
• The main issue of the collaborative filtering technique is that
it suffer from the cold start problem.
• Content based models are only good when recommending
items which are related to the same categories.
• The proposed system is a hybrid which solves the cold start
problem by including collaborative, content and age based
demographic level recommendations.
• For example, a new user of the kid's category of age 7 – 14
will be suggested with items which are similar to other kids of
the category that interacted the most and not anything from the
adult category.

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Research Design

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Dataset Details
• GroupLens Research project provides an open dataset of the
Movielens website(movielens.umn.edu) from September 19th,
1997 through April 22nd, 1998.
• Movielens 100k dataset. The data set consists of 100,000
ratings (1-5) from 943 users on 1682 movies.
• There are 3 different datasets available for user details, movie
details and ratings.
•https://grouplens.org/datasets/movielens/

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Dataset Details cont.

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Pre Processing
• Users are extracted with the condition of 20 ratings per
movie.
• The users are separated according to categories with
respect to their age group.
• We make use of demographic information of the users and
split them into Children, Teenager, Young Adult, Adult, Middle
Aged and Elderly categories.
• The user's has a minimum age of 7 and maximum age of
73.

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Pre Processing

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Filtering Techniques

21. Similarity Measures
Cosine
Similarity
• It is the cosine
of the angle
between the
two vectors
Dot product
• It is the cosine
of the angle
multiplied by
the product of
norms
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Collaborative Based filtering
Matrix Factorization based on SVD++ from surprise library is
used to get the initial set of recommendation for the existing
users.

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Demography Based filtering
• A new user’s category is identified based on the
categories of the existing user.
• The recommended collaborative results of the extracted
users are combined together.

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Content Based filtering
• The cosine similarity betweent the extracted movies are
taken to match for a best recommendation set

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Evaluation
• The base collaborative algorithm produced a
RMSE of 0.9351

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Limitations
• For larger datasets the demography filter
applied should be defined separatly.
• Larger datasets of the movielens does not
provide user demography details.
• On larger datasets the collaborative filtering
and cosine similarity measures will be
computational intensive.

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Conclusion
• Recommendation systems developed using this strategy
can be applied to show recommendations to new users in
terms of their age category.
• It solves the Cold start Problem.

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Future Scope
• The proposed model can be further used with high
dimensional dataset (say 10M Movielens dataset)
• The system is good at recommending movies within the
cluster of user categories by incorporating similar users
tastes.
• The proposed work is a general solution to any cold start
problem can be extended to any domain existing systems.

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References
1. Chikhaoui, B., Chiazzaro, M., & Wang, S. (2011, March). An improved hybrid
recommender system by combining predictions. In 2011 IEEE Workshops of
International Conference on Advanced Information Networking and
Applications (pp. 644-649). IEEE.
2. Santos, E. B., Garcia Manzato, M., & Goularte, R. (2014). Evaluating the
impact of demographic data on a hybrid recommender model . IADIS
International Journal on WWW/Internet, 12(2)(pp. 149-167).
3. Ghazanfar, M. A., & Prugel-Bennett,A. (2010, January). A scalable, accurate
hybrid recommender system. In 2010 Third International Conference on
Knowledge Discovery and Data Mining (pp. 94-98). IEEE.
4. Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic
literature review. Intelligent Data Analysis, 21(6), (pp.1487-1524).
5. Alshammari, G., Kapetanakis, S., Alshammari, A., Polatidis, N., & Petridis, M.
(2019). Improved movie recommendations based on a hybrid feature
combination method. Vietnam Journal of Computer Science, 6(03), (pp. 363-
376).
6. Hug, N. (2020). Surprise: A python library for recommender systems. Journal
of Open Source Software, 5(52), (pp.2174- 2177).

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References
7. GS, A. (2020). A Movie and Book Recommender System using Surprise
Recommendation.
8. Glauber, R., & Loula, A. (2019). Collaborative filtering vs. content-based
filtering: differences and similarities. arXiv preprint arXiv:1912.08932.
9. Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: History and
context. Acm transactions on interactive intelligent systems (tiis), 5(4), (pp. 1-
19)
10.Dataset Accessed on 12-12 2020:https://grouplens.org/datasets/movielens/

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Questions?