WELCOME

Project Name : Recommender

System
 Introduction
Recommendation systems are a subclass of information filtering systems. A
recommender system's main goal is to provide the user with software
suggestions for items that can be useful. The suggestions are related to
different decision-making mechanisms, and different techniques, such as,
what product to buy, what movie to watch, or what vacation to reserve. In
the context of recommender systems, the general term “item” refers to
what the system is actually recommending to its users. Many online
businesses rely on customer reviews and ratings. Explicit feedback is
especially important in the entertainment and eCommerce industry where
all customer engagements are impacted by these ratings. A
recommendation engine filters the data using different algorithms and
recommends the most relevant items to users. It first captures the past
behavior of a customer and based on that, recommends products that the
users might be likely to buy.
What are recommender systems ?
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.
Sites like Spotify, YouTube or Netflix use that data in order to suggest playlists, so-
called Daily mixes, or to make video recommendations, respectively.
Why do we need recommender systems?
Companies using recommender systems focus on increasing sales as a result of very
personalized offers and an enhanced customer experience.
Recommendations typically speed up searches and make it easier for users to access
content they’re interested in, and surprise them with offers they would have never
searched for.
What is more, companies are able to gain and retain customers by sending out emails with
links to new offers that meet the recipients' interests, or suggestions of films and TV shows
that suit their profiles.
The user starts to feel known and understood and is more likely to buy additional products
or consume more content. By knowing what a user wants, the company gains competitive
advantage and the threat of losing a customer to a competitor decreases.
Providing that added value to users by including recommendations in systems and products
is appealing. Furthermore, it allows companies to position ahead of their competitors and
eventually increase their earnings.
 How does a recommender
system work?
Recommender systems function with two kinds of information:
Characteristic information. This is information about items (keywords, categories,
etc.) and users (preferences, profiles, etc.).
User-item interactions. This is information such as ratings, number of purchases,
likes, etc.
Based on this, we can distinguish between three algorithms used in recommender
systems:
Content-based systems, which use characteristic information.
Collaborative filtering systems, which are based on user-item interactions.
Hybrid systems, which combine both types of information with the aim of
avoiding problems that are generated when working with just one kind.
Next, we will dig a little deeper into content-based and collaborative filtering
systems and see how they are different.
 Content-based systems
These systems make recommendations using a user's item and profile features. They hypothesize
that if a user was interested in an item in the past, they will once again be interested in it in the
future. Similar items are usually grouped based on their features. User profiles are constructed
using historical interactions or by explicitly asking users about their interests. There are other
systems, not considered purely content-based, which utilize user personal and social data.
One issue that arises is making obvious recommendations because of excessive specialization
(user A is only interested in categories B, C, and D, and the system is not able to recommend items
outside those categories, even though they could be interesting to them).
Another common problem is that new users lack a defined profile unless they are explicitly asked
for information. Nevertheless, it is relatively simple to add new items to the system. We just need
to ensure that we assign them a group according to their features.
 Collaborative filtering systems
To address some of the limitations of content-based filtering, collaborative filtering
uses similarities between users and items simultaneously to provide recommendations. This
allows for serendipitous recommendations; that is, collaborative filtering models can recommend
an item to user A based on the interests of a similar user B. Furthermore, the embeddings can be
learned automatically, without relying on hand-engineering of features.
Thank you