H1 Aggarwal
- The entity to which the recommendation is provided is called the user.
- The product that is being recommended is called an item.
Recommendation analysis is often based on the previous interaction between users and items, because
past interests and proclivities are often good indicators of future choices.
The basic principle of recommendations is there are notable dependencies between user and
item-centric activity. For example, a user who is interested in a historical documentary is more likely
to be interested in another historical documentary or an educational program, rather than in an action
movie.
The aforementioned description is based on a very simple family of recommendation algorithms,
referred to as neighbourhood models. This family belongs to a broader class of models, referred to as
collaborative filtering. The term "collaborative filtering" refers to the use of ratings from multiple
users in a collaborative way to predict missing ratings.
- In content-based recommender systems, the content plays a primary role in the recommendation
process, where the ratings of users and the attribute descriptions of items are leveraged in order to
make predictions
- The basic idea is that user interests can be modelled on the basis of properties (or attributes) of
the items they have rated or accessed in the past.
The purchase or browsing behavior of a user can be viewed as a type of implicit rating, as opposed to
an explicit rating, which is specified by the user. Many commercial systems allow the flexibility of
providing recommendations both on the basis of explicit and implicit feedback.
Presenting meaningful explanations is important to provide the user with an understanding of why
they might find a particular movie interesting. This approach also makes it more likely for the user to
act on the recommendation and truly improves the user experience.
The act of a user clicking on a news article can be viewed as a positive rating for that article. Such
ratings can be viewed as unary ratings, in which a mechanism exists for a user to express their
affinity for an item, but no mechanism exists for them to show their dislike.
Social networks are heavily dependent on the growth of the network to increase their advertising
revenues. Therefore, the recommendation of potential friends (or links) enables better growth and
connectivity of the network. This problem is also referred to as link prediction in the field of social
network analysis. Such forms of recommendations are based on structural relationships rather than
ratings data.
Two ways in which the recommendation problem may be formulated:
1. Prediction version of the problem: predicting the rating value for a user-item combination.
Uses training data that indicates user preferences for items. For m users and n items, the result
is a m x n matrix, where the missing values are predicted using the training model.
2. Ranking version of the problem: it is not necessary to predict the ratings of users for specific
items in order to make recommendations, rather it can be able to recommend the top-k items
for a particular user.
Common operational and technical goals of recommender systems are as follows:
1. Relevance: an operational goal of a recommender system is to recommend items that are relevant
to the user. Users are more likely to consume items they find interesting
2. Novelty: Recommender systems are truly helpful when the recommended item is something that
the user has not seen in the past. For example, popular movies of a preferred genre would rarely
, be novel to the user. Repeated recommendation of popular items can also lead to reduction in
sales diversity
3. Serendipity: A related notion is that of serendipity, wherein the items recommended are somewhat
unexpected, and therefore there is an element of lucky discovery as opposed to obvious
recommendations.
4. Increasing recommendation diversity: Recommender systems typically suggest a list of top-k items.
When all these recommended items are very similar, it increases the risk that the user might not like any of
these items. On the other hand, when the recommended list contains items of different types, there is a
greater chance that the user might like at least one of these items. Diversity has the benefit of ensuring that
the user does not get bored by repeated recommendation of similar items.
Soft goals that are met by the recommendation process:
- For the user: helps improves overall user satisfaction with the website. This improves user loyalty
and further increases sales
- For the merchant: the recommendation can provide insights into the needs of the user and help the
user experience further.
Basic models of recommender systems
Two types of models for recommender systems:
- Collaborative filtering: works with user-item interactions such as ratings or buying behavior
- Content-based filtering: works with the attribute information about the users and items such as
textual profiles or relevant keywords
Collaborative filtering models use the collaborative power of the ratings provided by multiple users to
make recommendations. The main challenge in designing collaborative filtering methods is that the
underlying ratings matrices are sparse. As a result, most of the ratings are unspecified. The specified
ratings are also referred to as observed ratings. The basic idea of collaborative filtering methods is
that these unspecified ratings can be interpolated because the observed ratings are often highly
correlated across various users and items.
There are two types of methods that are commonly used in collaborative filtering:
1. Memory-based methods: Memory-based methods are also referred to as neighbourhood based
collaborative filtering algorithms. The ratings of user-item combinations are predicted on the
basis of their neighbourhoods. These neighbourhoods can be defined in one of two ways:
a. User-based collaborative filtering: In this case, the ratings provided by like-minded users
of a target user A are used in order to make the recommendations for A. Thus, the basic
idea is to determine users, who are similar to the target user A, and recommend ratings for
the unobserved ratings of A by computing weighted averages of the ratings of this peer
group. Similarity functions are computed between the rows of the ratings matrix to
discover similar users
b. Item-based collaborative filtering: In order to make the rating predictions for target item
B by user A, the first step is to determine a set S of items that are most similar to target
item B. The ratings in item set S, which are specified by A, are used to predict whether
the user A will like item B. Similarity functions are computed between the columns of the
ratings matrix to discover similar items.
2. Model-based methods: In model-based methods, machine learning and data mining methods are
used in the context of predictive models.
- The entity to which the recommendation is provided is called the user.
- The product that is being recommended is called an item.
Recommendation analysis is often based on the previous interaction between users and items, because
past interests and proclivities are often good indicators of future choices.
The basic principle of recommendations is there are notable dependencies between user and
item-centric activity. For example, a user who is interested in a historical documentary is more likely
to be interested in another historical documentary or an educational program, rather than in an action
movie.
The aforementioned description is based on a very simple family of recommendation algorithms,
referred to as neighbourhood models. This family belongs to a broader class of models, referred to as
collaborative filtering. The term "collaborative filtering" refers to the use of ratings from multiple
users in a collaborative way to predict missing ratings.
- In content-based recommender systems, the content plays a primary role in the recommendation
process, where the ratings of users and the attribute descriptions of items are leveraged in order to
make predictions
- The basic idea is that user interests can be modelled on the basis of properties (or attributes) of
the items they have rated or accessed in the past.
The purchase or browsing behavior of a user can be viewed as a type of implicit rating, as opposed to
an explicit rating, which is specified by the user. Many commercial systems allow the flexibility of
providing recommendations both on the basis of explicit and implicit feedback.
Presenting meaningful explanations is important to provide the user with an understanding of why
they might find a particular movie interesting. This approach also makes it more likely for the user to
act on the recommendation and truly improves the user experience.
The act of a user clicking on a news article can be viewed as a positive rating for that article. Such
ratings can be viewed as unary ratings, in which a mechanism exists for a user to express their
affinity for an item, but no mechanism exists for them to show their dislike.
Social networks are heavily dependent on the growth of the network to increase their advertising
revenues. Therefore, the recommendation of potential friends (or links) enables better growth and
connectivity of the network. This problem is also referred to as link prediction in the field of social
network analysis. Such forms of recommendations are based on structural relationships rather than
ratings data.
Two ways in which the recommendation problem may be formulated:
1. Prediction version of the problem: predicting the rating value for a user-item combination.
Uses training data that indicates user preferences for items. For m users and n items, the result
is a m x n matrix, where the missing values are predicted using the training model.
2. Ranking version of the problem: it is not necessary to predict the ratings of users for specific
items in order to make recommendations, rather it can be able to recommend the top-k items
for a particular user.
Common operational and technical goals of recommender systems are as follows:
1. Relevance: an operational goal of a recommender system is to recommend items that are relevant
to the user. Users are more likely to consume items they find interesting
2. Novelty: Recommender systems are truly helpful when the recommended item is something that
the user has not seen in the past. For example, popular movies of a preferred genre would rarely
, be novel to the user. Repeated recommendation of popular items can also lead to reduction in
sales diversity
3. Serendipity: A related notion is that of serendipity, wherein the items recommended are somewhat
unexpected, and therefore there is an element of lucky discovery as opposed to obvious
recommendations.
4. Increasing recommendation diversity: Recommender systems typically suggest a list of top-k items.
When all these recommended items are very similar, it increases the risk that the user might not like any of
these items. On the other hand, when the recommended list contains items of different types, there is a
greater chance that the user might like at least one of these items. Diversity has the benefit of ensuring that
the user does not get bored by repeated recommendation of similar items.
Soft goals that are met by the recommendation process:
- For the user: helps improves overall user satisfaction with the website. This improves user loyalty
and further increases sales
- For the merchant: the recommendation can provide insights into the needs of the user and help the
user experience further.
Basic models of recommender systems
Two types of models for recommender systems:
- Collaborative filtering: works with user-item interactions such as ratings or buying behavior
- Content-based filtering: works with the attribute information about the users and items such as
textual profiles or relevant keywords
Collaborative filtering models use the collaborative power of the ratings provided by multiple users to
make recommendations. The main challenge in designing collaborative filtering methods is that the
underlying ratings matrices are sparse. As a result, most of the ratings are unspecified. The specified
ratings are also referred to as observed ratings. The basic idea of collaborative filtering methods is
that these unspecified ratings can be interpolated because the observed ratings are often highly
correlated across various users and items.
There are two types of methods that are commonly used in collaborative filtering:
1. Memory-based methods: Memory-based methods are also referred to as neighbourhood based
collaborative filtering algorithms. The ratings of user-item combinations are predicted on the
basis of their neighbourhoods. These neighbourhoods can be defined in one of two ways:
a. User-based collaborative filtering: In this case, the ratings provided by like-minded users
of a target user A are used in order to make the recommendations for A. Thus, the basic
idea is to determine users, who are similar to the target user A, and recommend ratings for
the unobserved ratings of A by computing weighted averages of the ratings of this peer
group. Similarity functions are computed between the rows of the ratings matrix to
discover similar users
b. Item-based collaborative filtering: In order to make the rating predictions for target item
B by user A, the first step is to determine a set S of items that are most similar to target
item B. The ratings in item set S, which are specified by A, are used to predict whether
the user A will like item B. Similarity functions are computed between the columns of the
ratings matrix to discover similar items.
2. Model-based methods: In model-based methods, machine learning and data mining methods are
used in the context of predictive models.
