11-12-2012, 01:11 PM
An Efficient System Based On Closed Sequential Patterns for Web Recommendations
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Abstract
Sequential pattern mining, since its introduction has
received considerable attention among the researchers
with broad applications. The sequential pattern algorithms
generally face problems when mining long sequential
patterns or while using very low support threshold. One
possible solution of such problems is by mining the closed
sequential patterns, which is a condensed representation of
sequential patterns. Recently, several researchers have
utilized the sequential pattern discovery for designing a
web recommendation system, which provides personalized
recommendations of web access sequences for users. This
paper describes the design of a web recommendation
system for providing recommendations to a user‘s web
access sequence. The proposed system is mainly based on
mining closed sequential web access patterns. Initially, the
PrefixSpan algorithm is employed on the preprocessed
web server log data for mining sequential web access
patterns. Subsequently, with the aid of post-pruning
strategy, the closed sequential web access patterns are
discovered from the complete set of sequential web access
patterns.
Introduction
The development of data mining techniques has been
centralized on discovering hidden data in an efficient way
that is beneficial for corporate decision-makers [1, 2].
Sequential pattern mining is an important subject of data
mining which is extensively applied in several areas [3]. In
general, sequential pattern mining is defined as
determining the complete set of frequent subsequences in
a set of sequences [4, 13]. Sequential pattern is a sequence
of itemsets that frequently appear in a particular order,
such that, all items in the same itemset are expected to
have the same transaction time value or within a time gap.
Each sequence relates to a temporally ordered list of
events, where each event is considered as a collection of
items/itemset occurring at the same time [5]. Generally, all
the transactions of a customer are collectively considered
as a sequence, known as customer-sequence, where each
transaction is denoted as an item set in that sequence and
all the transactions are listed in a particular order in
connection with the transaction-time [6].
Review of Related Research
Numerous researches are available in the literature for web
recommendation system using sequential pattern mining.
It looks more preferable if we design web
recommendation system based on closed sequential
patterns for its compact representation. Here, we present
some of the researches related with closed sequential
pattern mining along with web recommendation system
based on sequential pattern mining.
Zhou. B et al. [26] have proposed an intelligent Web
recommender system identified as SWARS (sequential
Web access-based recommender system) that employs
sequential access pattern mining. In the proposed system,
CS-mine, an efficient sequential pattern mining algorithm
was made use of to recognize frequent sequential Web
access patterns. The access patterns were then stored in a
compact tree structure (Pattern-tree) which was then
employed for matching and generating Web links for
recommendations. The performance of the proposed
system was assessed on the basis of precision, satisfaction
and applicability. An efficient sequential access pattern
mining algorithm, called CSB-mine (Conditional
Sequence Base mining algorithm) was presented by
Baoyao Zhou et al. [27]. The presented CSB-mine
algorithm was on the basis of conditional sequence bases
of each frequent event which removes the need for
constructing WAP-trees.
A Proposed Web Recommendation System Based
On Closed Sequential Patterns
Web Personalization is an application of data mining and
machine learning techniques to build models of user
behavior. It can be useful to the task of predicting user
needs and adapting future interactions with the main aim
of improved user satisfaction [22]. A unique and important
class of personalized Web applications is represented by
Web recommendation systems. It highlights on the userdependent
filtering and selection of relevant information.
Several approaches like Content-Based Filtering,
Clustering Based Approaches, Graph Theoretic
Approaches, Association and Sequence Rule Based
Approaches are available in the literature for designing
web recommendation system [22].
Preprocessing
The purpose of data preprocessing is to extract useful data
from raw web log and then transform these data in to the
form necessary for pattern discovery. Due to large amount
of irrelevant information in the web log, the original log
cannot be directly used in the web log mining procedure,
hence in data preprocessing phase, raw Web logs need to
be cleaned, analyzed and converted for further step. A
Web log is a file to which the Web server writes
information each time a user requests a resource from that
particular site. Most logs use the format of the common
log format. Each entry in the log file consists of a
sequence of fields relating to a single HTTP transaction
with the various fields.
Conclusion
In this paper, we have devised a web recommendation
system based on closed sequential pattern mining. In the
proposed system, we have employed PrefixSpan (pattern
growth algorithm) for mining the sequential web access
patterns from the preprocessed web server log data and the
closed sequential web access patterns are obtained from
the mined sequential web access pattern. Then, Patricia
trie based data structure is used to build the pattern tree,
where mined closed sequential web access patterns are
stored. The target web link is given to the new user by
matching the pattern tree with the new user’s current web
access sequence. The proposed web recommendation
system is validated with the synthetic dataset and the
experimental results showed that the proposed system
outperformed with good precision, applicability and hit
ratio.