Recommender System _Module 1_Introduction to Recommender System.pptx

Subject: Recommender System
Module 01 Name :- Introduction to Recommender System

Introduction to Recommender System
Aim:
To facilitate students to understand the importance and basic concepts of recommender system in data science,
recommender system as multi-disciplinary field, emerging topics and challenges in recommender system.
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Syllabus:
• Introduction to recommender system
• understanding recommender system,
• kinds of recommender systems: -
(i)collaborative filtering recommender system,
(ii) content based recommender system,
• knowledge based recommender system
• hybrid system, application and evaluation techniques
• recommender
• human computer interaction,
• recommender system as multi-disciplinary field,
• emerging topics and challenges in recommender system
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Table of Content
 Introduction to recommender system
 understanding recommender system,
 kinds of recommender systems
 collaborative filtering recommender system
 content based recommender system
 knowledge based recommender system
 hybrid system
 application and evaluation techniques
 recommender
 human computer interaction,
 recommender system as multi-disciplinary field,
 emerging topics and challenges in recommender system
 Conclusion of Unit
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Objective:
Objective of this unit is to learn:-
 Learn basic concept of recommender system.
 Understand kinds of recommender systems.
 Analysis of different knowledge based recommender system, hybrid system, application and
evaluation techniques
 Evaluate recommender system as multi-disciplinary field.
 Understand emerging topics and challenges in recommender system
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Outcome:
Outcome:
Outcome after completion of this unit student able to:-
 Students will able how understand basic concept of recommender system.
 Students will able to used of different kinds of recommender systems.
 Students can evaluate different knowledge based recommender system.
 Students can evaluate recommender system as multi-disciplinary field.
 Students will able to basic requirement emerging topics and challenges in recommender system.
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Introduction to recommender system
During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services,
recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers
articles that could interest them) to online advertisement (suggest to users the right contents, matching their
preferences), recommender systems are today unavoidable in our daily online journeys
In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items
being movies to watch, text to read, products to buy or anything else depending on industries).
• Recommender systems aim to predict users’ interests and recommend product items that quite likely are
interesting for them. They are among the most powerful machine learning systems that online retailers
implement in order to drive sales.
• Data required for recommender systems stems from explicit user ratings after watching a movie or listening
to a song, from implicit search engine queries and purchase histories, or from other knowledge about the
users/items themselves.
• Source:https://tryolabs.com/blog/introduction-to-recommender-systems/
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understanding recommender system
• Recommender systems are so commonplace now that many of us use them without even knowing it.
Because we can't possibly look through all the products or content on a website, a recommendation system
plays an important role in helping us have a better user experience, while also exposing us to more
inventory we might not discover otherwise.
• Some examples of recommender systems in action include product recommendations on Amazon, Netflix
suggestions for movies and TV shows in your feed, recommended videos on YouTube, music on Spotify, the
Facebook newsfeed and Google Ads.
• Source:https://tryolabs.com/blog/introduction-to-recommender-systems/
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Source:https://builtin.com/data-science/recommender-systems

• important component of any of these systems is the recommender function, which takes information about
the user and predicts the rating that user might assign to a product, for example. Predicting user ratings,
even before the user has actually provided one, makes recommender systems a powerful tool.
• Source:https://builtin.com/data-science/recommender-systems
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kinds of recommender systems
HOW DO RECOMMENDER SYSTEMS WORK.
UNDERSTANDING RELATIONSHIPS
Relationships provide recommender systems with tremendous insight, as well as an understanding of
customers. There are three main types that occur:
• User-Product Relationship
• Product-Product Relationship
• User-User Relationship
• Source: https://builtin.com/data-science/recommender-systems
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(i)User-Product Relationship
The user-product relationship occurs when some users have an affinity or preference towards specific products
that they need. For example, a cricket player might have a preference for cricket-related items, thus the e-
commerce website will build a user-product relation of player->cricket.
(ii)Product-Product Relationship
Product-product relationships occur when items are similar in nature, either by appearance or description. Some
examples include books or music of the same genre, dishes from the same cuisine, or news articles from a
particular event.
• Source: https://builtin.com/data-science/recommender-systems
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User-User Relationship
User-user relationships occur when some customers have similar taste with respect to a particular product or
service. Examples include mutual friends, similar backgrounds, similar age, etc.
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Source: https://link.springer.com/chapter/10.1007/978-981-13-1595-4_12

collaborative filtering recommender system
Collaborative methods for recommender systems are methods that are based solely on the past interactions
recorded between users and items in order to produce new recommendations. These interactions are stored in
the so-called “user-item interactions matrix”.
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Source: https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada

In short, collaborative filtering systems are based on the assumption that if a user likes item A and another user
likes the same item A as well as another item, item B, the first user could also be interested in the second item.
Hence, they aim to predict new interactions based on historical ones. There are two types of methods to achieve
this goal:
• memory-based
• model-based
Memory based approaches directly works with values of recorded interactions, assuming no model, and are
essentially based on nearest neighbours search (for example, find the closest users from a user of interest and
suggest the most popular items among these neighbours). Model based approaches assume an underlying
“generative” model that explains the user-item interactions and try to discover it in order to make new
predictions.
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Two types of collaborative filtering techniques are used:
1. User-User collaborative filtering
2. Item-Item collaborative filtering
User-User collaborative filtering
In this, the user vector includes all the items purchased by the user and rating given for each particular product.
The similarity is calculated between users using an n*n matrix in which n is the number of users present. The
similarity is calculated using the same cosine similarity formula. Now, the recommending matrix is calculated.
In this, the rating is multiplied by the similarity between the users who have bought this item and the user to
which item has to be recommended. This value is calculated for all items that are new for that user and are
sorted in descending order. Then the top items are recommended to that user. If a new user comes or old user
changes his or her rating or provides new ratings then the recommendations may change.
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Source: https://towardsdatascience.com/brief-on-recommender-systems-b86a1068a4dd

Item-Item collaborative filtering
In this, rather than considering similar users, similar items are considered. If the user ‘A’ loves ‘Inception’ he
may like ‘The Martian’ as the lead actor is similar. Here, the recommendation matrix is m*m matrix where m
is the number of items present.
.
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Advantages
• New products can be introduced to the user.
• Business can be expanded and can popularise new products.
Disadvantages
• User’s previous history is required or data for products is required based on the type of collaborative method
used.
• The new item cannot be recommended if no user has purchased or rated it.
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content based recommender system
Content based methods
• Unlike collaborative methods that only rely on the user-item interactions, content based approaches use
additional information about users and/or items. If we consider the example of a movies recommender
system, this additional information can be, for example, the age, the sex, the job or any other personal
information for users as well as the category, the main actors, the duration or other characteristics for the
movies (items).
• This filtering is based on the description or some data provided for that product. The system finds the
similarity between products based on its context or description. The user’s previous history is taken into
account to find similar products the user may like.
• For example, if a user likes movies such as ‘Mission Impossible’ then we can recommend him the movies of
‘Tom Cruise’ or movies with the genre ‘Action’.
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• Advantages
• The user gets recommended the types of items they love.
• The user is satisfied by the type of recommendation.
• New items can be recommended; just data for that item is required.
Disadvantages
• The user will never be recommended for different items.
• Business cannot be expanded as the user does not try a different type of product.
• If the user matrix or item matrix is changed the cosine similarity matrix needs to be calculated again.

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• collaborative filtering: “recommend items that similar users liked”
• content based: “recommend items that are similar to those the user liked in the past”

knowledge based recommender system
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• A recommender system is knowledge-based when it makes recommendations based not on a user’s
rating history, but on specific queries made by the user. It might prompt the user to give a series of rules
or guidelines on what the results should look like, or an example of an item. The system then searches
through its database of items and returns similar results.
• The user begins by making a query. As I mentioned earlier, this could be by constraints (constraint-
based) or by example (case-based).
• The first thing our algorithm will do is search our domain knowledge for relevant rules. Our domain
knowledge is a library of “if-this-then-that” rules that are specific to our “domain”. In the domain of
automobiles, a rule might look like: “new” → “Date: Within a year ago”. Note that we’re mapping a
high-level attribute to requirement for item attributes. In the event that we find a relevant rule, the
consequent is added to the query, and we repeat the process until all relevant rules are found.

knowledge based recommender system
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• Once all of the requirements are found, they are combined to form a single database query. This
selection query is used to retrieve relevant items from database.
• The user might “critique” the results — tightening and loosening parameters, or selecting the best items
and changing specifications. This creates a new query, and the process repeats until the user finds a
satisfactory item.

Hybrid system recommender systems
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Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based
filtering, and other approaches .
There is no reason why several different techniques of the same type could not be hybridized. Hybrid
approaches can be implemented in several ways: by making content-based and collaborative-based
predictions separately and then combining them; by adding content-based capabilities to a collaborative-
based approach (and vice versa).
Several studies that empirically compare the performance of the hybrid with the pure collaborative and
content-based methods and demonstrated that the hybrid methods can provide more accurate
recommendations than pure approaches.
These methods can also be used to overcome some of the common problems in recommender systems
such as cold start and the sparsity problem, as well as the knowledge engineering bottleneck in
knowledge-based approaches.

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• Some hybridization techniques include:
• Weighted: Combining the score of different recommendation components numerically.
• Switching: Choosing among recommendation components and applying the selected one.
• Mixed: Recommendations from different recommenders are presented together to give the recommendation.
• Feature Combination: Features derived from different knowledge sources are combined together and given to
a single recommendation algorithm.
• Feature Augmentation: Computing a feature or set of features, which is then part of the input to the next
technique.
• Cascade: Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the
higher ones.
• Meta-level: One recommendation technique is applied and produces some sort of model, which is then the
input used by the next technique

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Source: https://slidetodoc.com/hybrid-recommendation-approaches-1-hybrid-recommender-systems-hybrid-2/

recommender systems Application and evaluation techniques
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Source: https://slidetodoc.com/hybrid-recommendation-approaches-1-hybrid-recommender-systems-hybrid-2/
1. “Improving with use” (retention): One of the core potential benefits of recommendation systems is their
ability to continuously calibrate to the preferences of the user.
2. Improving cart value: A company with an inventory of thousands and thousands of items would be hard
pressed to hard-code product suggestions for all of it’s products, and it’s obvious that such static suggestions
would quickly be out-of-date or irrelevant for many customers. By using various means of “filtering”,
eCommerce giants can find opportune times to suggest (on their site, via email, or though other means) new
products that you’re likely to buy.
3 . Improved engagement and delight: Sometimes seeing an ROI doesn’t involve explicitly asking for
payment. Many companies use these systems to simply encourage engagement and activity on their product or
platform

Recommender systems evaluation techniques
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Source: https://medium.com/the-owl/evaluating-recommender-systems-749570354976

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We first split the data into training set and testing set. Usually the training set is much bigger than the
testing set.
Now we training our recommender system using the training set only.
This is where it obtains the relationship between items and users.
Once its trained we can ask it to make predictions about how a new user might rate some of the items
they have never seen before.if we really want to get fancy, its possible to improve train/test splits by a
technique called K-fold Cross Validation.Its the same idea as train/test but instead of a single training
set we create many randomly assigned training sets.Each individual training set or fold is used to train
our recommender system independently and then we measure the accuracy of the resulting systems
against our test set.So we end up with a score how accurately each fold ends up predicting user ratings
and we can average them together. This obviously takes a lot more computing power to do but the
advantage is we donot end up in overfitting to a single training set. Here’s how we do it :-

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To reiterate, train/test and k-fold cross-validation are ways to measure the accuracy of our recommender
system.
That is, how accurately we can predict how users rated movies they have already seen and provided a rating
for. By using train/test, all we can do is test our ability to predict how people rated movies they already saw.
That’s not the point of a recommender system. We want to recommend new things to people that they haven’t
seen, but find interesting. However, that’s fundamentally impossible to test offline.

human computer interaction
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Human-computer interaction (HCI) is a multidisciplinary field of study focusing on the design of computer
technology and, in particular, the interaction between humans (the users) and computers. While initially
concerned with computers, HCI has since expanded to cover almost all forms of information technology
design.
HCI would expand to incorporate multiple disciplines, such as computer science, cognitive science and human-
factors engineering.

Recommender system as multi-disciplinary field
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 Recommender system uses AI with ML
 Recommender system uses in Education with ML
 Recommender system uses in healthcare with ML
 Recommender system uses in Deep learning with ML
 Recommender system uses in Deep learning with ML

Emerging topics and challenges in recommender system
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challenges in recommender system
 Lack of Data
 Changing Data
 Changing User Preferences
 Unpredictable Items
 This Stuff is Complex!
Research topic 1: recommendation system and deep learning
In recent years, deep learning technology has achieved great success in areas of speech recognition,
computer vision, and natural language processing;
and recommendation systems can benefit from these breakthroughs. Today, deep learning-based
recommendation algorithms have made remarkable progress in the following three aspects:

Emerging topics and challenges in recommender system
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Source: https://www.microsoft.com/en-us/research/lab/microsoft-research-asia/articles/personalized-recommendation-systems/
Research topic 2: recommendation system and knowledge graph
In most recommendation scenarios, items may contain rich knowledge information. The network structure
that captures such knowledge is referred to as the knowledge graph.
Research topic 3: recommendation system and reinforcement learning:Empowered by the latest
techniques on deep learning and the knowledge graph, recommendation systems have been increasing in
performance. However, most of the existing recommendation systems are formulated in a one-way fashion:
given sufficiently collected historical data, a specific type of supervised learning model (such as linear
regression or factorization machine), is trained to capture the underlying preferences of users over difference
kinds of items

MCQ:
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Q.1 What is/are the advantage/s of Recommender Systems ?
A. Recommender Systems provide a better experience for the users by giving them a broader exposure to
many different products they might be interested in.
B. Recommender Systems encourage users towards continual usage or purchase of their product
C. Recommender Systems benefit the service provider by increasing potential revenue and better security
for its consumers.
D. All the above
Answer: D

MCQ:
38
Q.2 What are advantage of content based recommender system.
A. The user gets recommended the types of items they love.
B. The user is satisfied by the type of recommendation.
C. New items can be recommended; just data for that item is required
D. All the above
Answer: D

MCQ:
39
Q 3. “The new item cannot be recommended if no user has purchased or rated it” this is true/ false for
collaborative fiter recommender system
(i) True (ii) False
Answer : False

MCQ:
40
Q 4. An expert system differs from a database program in that only an expert system _____________
a) contains declarative knowledge
b) contains procedural knowledge
c) features the retrieval of stored information
d) expects users to draw their own conclusions
Answer: (b)

Short Answer :
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Q5 . What is a content-based recommendation system?
Answer :Content-based recommendation system tries to recommend items to the users based on their profile
built upon their preferences and taste.

Short Answer :
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Q.6 What is the meaning of “Cold start” in collaborative filtering?
Answer : The difficulty in recommendation when we have new user, and we cannot make a profile for him,
or when we have a new item, which has not got any rating yet

Short Answer :
43
Q.7 What is a “Memory-based” recommender system?
Answer : In memory based approach, we use the entire user-item dataset to generate a recommendation
system.

Assignment
I. What is Recommender system ?
II. Difference between collaborative vs content based recommender system?
III. Write a short note on knowledge based Recommender system
IV. Explain limitation of Recommender system ?
V. Why HCI play important role in Recommender system ?
VI. Explain Recommender system evaluation technique in detail ?
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Summary
 The motivation behind a recommender framework is to propose pertinent things to clients. To
accomplish this assignment, there exist two significant classes of techniques : shared separating
strategies and substance based strategies.
 knowledge based recommender system are a particular kind of recommender framework that
depend on unequivocal information about the thing grouping, client inclinations, and
suggestion models
 content based recommender systemutilizes thing highlights to prescribe different things like
what the client likes, in view of their past activities or express input
 collaborative filtering recommender system separating is a technique for making programmed
expectations about the interests of a client by gathering inclinations or taste data from
numerous clients

Documents Links
Document Link
1. Recommender Systems: An Overview https://www.researchgate.net/publication/22060
4600_Recommender_Systems_An_Overview
2. Knowledge-Based Recommender Systems https://www.researchgate.net/publication/23
78325_Knowledge-
Based_Recommender_Systems
3. Collaborative Filtering Recommender System:
Overview and Challenges
https://www.researchgate.net/publication/32
0761149_Collaborative_Filtering_Recommende
r_System_Overview_and_Challenges

Introduction to Web Security
• Video Links
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1 Use Cases of Recommendation Systems in
Business – Current Applications and Methods
https://emerj.com/ai-sector-overviews/use-cases-
recommendation-systems/
2. Movie Recommendation System with
Collaborative Filtering
https://www.youtube.com/watch?v=3ecNC-So0r4
3. A Knowledge Based Recommendation System
That Includes Sentiment Analysis and Deep
Learning
https://www.youtube.com/watch?v=zsd7uq4KngE
1. Use Cases of Recommendation Systems in
Business – Current Applications and Methods
https://emerj.com/ai-sector-overviews/use-cases-
recommendation-systems/

Introduction to Web Security
• E- Resource
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Topic URL
Recommender Systems Handbook Francesco Ricci ·
Lior Rokach · Bracha Shapira · Paul B. Kantor Editors
https://www.cse.iitk.ac.in/users/nsrivast/HCC/Recom
mender_systems_handbook.pdf
Recommendation Systems http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
Recommendation systems: Principles, methods and
evaluation
https://www.researchgate.net/publication/283180981_
Recommendation_systems_Principles_methods_and_
evaluation

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