13-08-2014, 10:06 AM
General Framework on Mining Web Graphs
for Recommendations
[attachment=66593]
Abstract –
As the exponential explosion of various contents generated on the Web, Recommendation techniques have become
increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies,
music, images, books recommendations, query suggestions, tags recommendations, etc. No matter what types of data sources are
used for the recommendations, essentially these data sources can be modeled in the form of various types of graphs. In this paper,
aiming at providing a general framework on mining Web graphs for recommendations, we first propose a novel diffusion method
which propagates similarities between different nodes and generates recommendations; then we illustrate how to generalize different
recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation
tasks on the World Wide Web, including query suggestions, tag recommendations, expert finding, image recommendations, image
annotations, etc
INTRODUCTION
With the diverse and explosive growth of Web
information, how to organize and utilize the information
effectively and efficiently has become more and more
critical. This is especially important for Web 2.0 related
applications since user generated information is more
free-style and less structured, which increases the
difficulties in mining useful information from these data
sources. In order to satisfy the information needs of
Web users and improve the user experience in many
Web applications, Recommender Systems, have been
well studied in academia and widely deployed in
industry.
Typically, recommender systems are based on
Collaborative Filtering, this is a technique that
automatically predicts the interest of an active user by
collecting rating information from other similar users or
items. The underlying assumption of collaborative
filtering is that the active user will prefer those items
which other similar users prefer.
Fortunately, on the Web, no matter what types of
data sources are used for recommendations, in most
cases, these data sources can be modeled in the form of
various types of graphs. If we can design a general
graph recommendation algorithm, we can solve many
recommendation problems on the Web. However, when
designing such a framework for recommendations on
the Web, we still face several challenges that need to be
addressed. The first challenge is that it is not easy to
recommend latent semantically relevant results to users.
The second challenge is how to take into account the
personalization feature. The last challenge is that it is
time-consuming and inefficient to design different
recommendation algorithms for different
recommendation tasks. Actually, most of these
recommendation problems have some common features,
where a general framework is needed to unify the
recommendation tasks on the Web. Moreover, most of
existing methods are complicated and require to tune a
large number of parameters.
In this, aiming at solving the problems analyzed
above, we propose a general framework for the
recommendations on the Web. This framework is built
upon the heat diffusion on both undirected graphs and
directed graphs, and has several advantages: (1) It is a
general method, which can be utilized to many
recommendation tasks on the Web; (2) It can provide
latent semantically relevant results to the original
information need; (3) This model provides a natural
treatment for personalized recommendations; (4) The
designed recommendation algorithm is scalable to very
large datasets
Overview of the Collaborative Filtering Progress
The goal of a collaborative filtering algorithm is to
suggest new items or to predict the utility of a certain
item for a particular user based on the user’s previous
likings and the opinions of other like-minded users. In a
typical CF scenario, there is a list of m users
U={u1,u2,...um} and a list of n items I={i1,i2,...in}.
Each user ui has a list of items Iui, which the user has
expressed his/her opinions about.
Opinions can be explicitly given by the user as a
rating score, generally within a certain numerical scale,
or can be implicitly derived from purchase records, by
analyzing timing logs, by mining web hyperlinks and so
on. Note that Iui I and it is possible for Iui to be a
null-set. There exists a distinguished user Ua U
called the acive user for whom the task of a
collaborative filtering algorithm is to find an item
likeliness that can of two forms.
Social Recommendation
Since our model is quite general, we can apply it to
more complicated graphs and applications, such as
Social Recommendation problem. Recently, as the
explosive growth of Web 2.0 applications, social-based
applications gain lots of traffics on the Web. Social
recommendation, which produces recommendations by
incorporating users’ social network information, is
becoming to be an indispensable feature for the next
generation of Web applications.
for Recommendations
[attachment=66593]
Abstract –
As the exponential explosion of various contents generated on the Web, Recommendation techniques have become
increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies,
music, images, books recommendations, query suggestions, tags recommendations, etc. No matter what types of data sources are
used for the recommendations, essentially these data sources can be modeled in the form of various types of graphs. In this paper,
aiming at providing a general framework on mining Web graphs for recommendations, we first propose a novel diffusion method
which propagates similarities between different nodes and generates recommendations; then we illustrate how to generalize different
recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation
tasks on the World Wide Web, including query suggestions, tag recommendations, expert finding, image recommendations, image
annotations, etc
INTRODUCTION
With the diverse and explosive growth of Web
information, how to organize and utilize the information
effectively and efficiently has become more and more
critical. This is especially important for Web 2.0 related
applications since user generated information is more
free-style and less structured, which increases the
difficulties in mining useful information from these data
sources. In order to satisfy the information needs of
Web users and improve the user experience in many
Web applications, Recommender Systems, have been
well studied in academia and widely deployed in
industry.
Typically, recommender systems are based on
Collaborative Filtering, this is a technique that
automatically predicts the interest of an active user by
collecting rating information from other similar users or
items. The underlying assumption of collaborative
filtering is that the active user will prefer those items
which other similar users prefer.
Fortunately, on the Web, no matter what types of
data sources are used for recommendations, in most
cases, these data sources can be modeled in the form of
various types of graphs. If we can design a general
graph recommendation algorithm, we can solve many
recommendation problems on the Web. However, when
designing such a framework for recommendations on
the Web, we still face several challenges that need to be
addressed. The first challenge is that it is not easy to
recommend latent semantically relevant results to users.
The second challenge is how to take into account the
personalization feature. The last challenge is that it is
time-consuming and inefficient to design different
recommendation algorithms for different
recommendation tasks. Actually, most of these
recommendation problems have some common features,
where a general framework is needed to unify the
recommendation tasks on the Web. Moreover, most of
existing methods are complicated and require to tune a
large number of parameters.
In this, aiming at solving the problems analyzed
above, we propose a general framework for the
recommendations on the Web. This framework is built
upon the heat diffusion on both undirected graphs and
directed graphs, and has several advantages: (1) It is a
general method, which can be utilized to many
recommendation tasks on the Web; (2) It can provide
latent semantically relevant results to the original
information need; (3) This model provides a natural
treatment for personalized recommendations; (4) The
designed recommendation algorithm is scalable to very
large datasets
Overview of the Collaborative Filtering Progress
The goal of a collaborative filtering algorithm is to
suggest new items or to predict the utility of a certain
item for a particular user based on the user’s previous
likings and the opinions of other like-minded users. In a
typical CF scenario, there is a list of m users
U={u1,u2,...um} and a list of n items I={i1,i2,...in}.
Each user ui has a list of items Iui, which the user has
expressed his/her opinions about.
Opinions can be explicitly given by the user as a
rating score, generally within a certain numerical scale,
or can be implicitly derived from purchase records, by
analyzing timing logs, by mining web hyperlinks and so
on. Note that Iui I and it is possible for Iui to be a
null-set. There exists a distinguished user Ua U
called the acive user for whom the task of a
collaborative filtering algorithm is to find an item
likeliness that can of two forms.
Social Recommendation
Since our model is quite general, we can apply it to
more complicated graphs and applications, such as
Social Recommendation problem. Recently, as the
explosive growth of Web 2.0 applications, social-based
applications gain lots of traffics on the Web. Social
recommendation, which produces recommendations by
incorporating users’ social network information, is
becoming to be an indispensable feature for the next
generation of Web applications.