Important: Use custom search function to get better results from our thousands of pages

Use " " for compulsory search eg:"electronics seminar" , use -" " for filter something eg: "electronics seminar" -"/tag/" (used for exclude results from tag pages)


 
 
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Mining Web Graphs for Recommendations
Post: #1

Mining Web Graphs for Recommendations


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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 usergenerated
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 [14], [22], [25], [41], [46], [49], which 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 [38].



RELATED WORK
Recommendation on the Web is a general term representing
a specific type of information filtering technique that attempts
to present information items (queries, movies, images, books,
Web pages, etc.) that are likely of interest to the users. In this
section, we review several work related to recommendation,
including collaborative filtering, query suggestion techniques,
image recommendation methods, and clickthrough data analysis.

2.1 Collaborative Filtering
Two types of collaborative filtering approaches are widely
studied: neighborhood-based and model-based.
The neighborhood-based approaches are the most popular
prediction methods and are widely adopted in commercial
collaborative filtering systems [37], [47]. The most analyzed
examples of neighborhood-based collaborative filtering include
user-based approaches [7], [21] and item-based approaches
[15], [37], [50]. User-based approaches predict the
ratings of active users based on the ratings of their similar
users, and item-based approaches predict the ratings of active
users based on the computed information of items similar
to those chosen by the active user.


2.2 Query Suggestion
In order to recommend relevant queries to Web users, a
valuable technique, query suggestion, has been employed by
some prominent commercial search engines, such as Yahoo!3,
Live Search4, Ask5 and Google6. However, due to commercial
reasons, few public papers have been released to reveal the
methods they adopt.
The goal of query suggestion is similar to that of query
expansion [11], [13], [56], [61], query substitution [31] and
query refinement [35], [57], which all focus on understanding
users’ search intentions and improving the queries submitted
by users. Query suggestion is closely related to query expansion
or query substitution, which extends the original query
with new search terms to narrow down the scope of the search.
But different from query expansion, query suggestion aims
to suggest full queries that have been formulated by previous
users so that query integrity and coherence are preserved in the
suggested queries [18]. Query refinement is another closelyrelated
notion, since the objective of query refinement is
interactively recommending new queries related to a particular
query.
Post: #2
Ppt web mining graphs for recommendation
Post: #3

By increasing various content on the web, the recommendation by the user will also be increased as well as techniques for those maintenance will also be very necessary. Several recommendations on the web are movies, news, music, books and images, etc. The number of recommendations is worldwide. This recommendation data modeled on data resources using different chart types. But these methods are less structured and more diverse. So here we have implemented a framework for mining recommendation in web graphics. Recommendations on the Web are a general term that represents a specific type of information filtering technique that attempts to present information elements (queries, movies, images, books, Web pages, etc.) that may interest users. With the diverse and explosive growth of Web information, how to organize and use information effectively and efficiently has become increasingly critical. This is especially important for applications related to Web 2.0, since the information generated by the user is freer and less structured, which increases the difficulties in extracting useful information from these data sources. In order to meet the information needs of web users and improve the user experience in many web applications, recommendation systems.

Normally, recommendation systems are based on collaborative filtering, which is a technique that automatically predicts the interest of an active user in collecting rating information from other users or similar elements. The underlying assumption of collaborative filtering is that the active user will prefer those elements that other similar users prefer. Based on this simple but effective intuition, collaborative filtering has been widely used in some major commercial systems known, including Amazon product recommendation, Netflix movie recommendation, etc. But, collaborative filtering algorithms require a user qualification matrix that contains user-specific rating preferences to infer user characteristics. However, in most cases, classification data is not always available, since information on the Web is less structured and more diverse.

Fortunately, on the Web, regardless of the types of data sources that are used for recommendations, in most cases, these data sources can be modeled as various types of graphs. If we can design a general algorithm of graphical recommendation, we can solve many problems of recommendation. However, in designing a framework for recommendations on the Web, there are several challenges. The first challenge is that it is not easy to recommend latent results that are semantically relevant to users. Take Suggestion for consultation as an example, there are several outstanding issues that can potentially degrade the quality of recommendations. The first is the ambiguity that commonly exists in natural language. For example, queries containing ambiguous terms may confuse algorithms that degrade users' information needs.

In addition, users tend to present brief queries consisting of only one or two terms in most circumstances, and short queries are more likely to be ambiguous. Added to that, in most cases, the reason users perform a search is because they have little or even no knowledge about the topic they are looking for. To find satisfactory answers, users have to restate their queries constantly. The second challenge is how to take into account the personalization function. Customization is desirable for many scenarios where different users have different information needs. For example, Amazon.com has been the first adopter of personalization technology to recommend products to buyers on their site, based on their previous purchases. Adopting personalization will not only filter irrelevant information to a person, but will also provide more specific information that is increasingly relevant to a person's interests.
The last challenge is that it is time-consuming and inefficient to design different recommendation algorithms for different recommendation tasks. Therefore, a general framework is needed to unify the recommendation tasks on the Web. In addition, most existing methods are complicated and require fine tuning of a large number of parameters.

It can be understood in the following video:



 

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