User Access Sequences Research Articles

E-Commerce User Type Recognition Based on Access Sequence Similarity

ABSTRACT In order to measure the similarity of non-equal length and non-numerical sequence effectively, in this paper, the access sequence similarity calculation method was proposed based on the characteristics of e-commerce user access sequence. The sliding window method was improved by increasing the similarity calculation of nodes and optimizing the sliding similarity calculation method. The key factor of Edit Distance on Real Sequences was optimized. It mainly includes the calculation method of increasing the similarity of nodes and optimizing the calculation method of sliding similarity; the calculation method of subcost in the editing distance of real sequences is optimized. Then, the optimized Edit Distance on Real Sequences was embedded into the improved sliding window method to replace the original distance calculation method. Based on the access sequence similarity calculation results, the clustering algorithm was used to get the e-commerce users type. The experimental results showed the following facts: The improved access sequence similarity algorithm can measure the similarity of non-numerical and non-equal length sequences more accurately; based on the similarity of access sequences, it is possible to divide the types of e-commerce users more effectively, besides the e-commerce users are mainly composed of young men, users’ online time shows obvious fragmentation characteristics, their online browsing behavior obeys long tail distribution, they still primarily buy hot items, and the e-commerce users can be divided into six categories.

An effective recommendation model using user access sequence and context entropy

An effective recommendation model using user access sequence and context entropy

Efficient 3-D Scene Prefetching From Learning User Access Patterns

Rendering large-scale 3-D scenes on a thin client is attracting increasing attention with the development of the mobile Internet. Efficient scene prefetching to provide timely data with a limited cache is one of the most critical issues for remote 3-D data scheduling in networked virtual environment applications. Existing prefetching schemes predict the future positions of each individual user based on user traces. In this paper, we investigate scene content sequences accessed by various users instead of user viewpoint traces and propose a user access pattern-based 3-D scene prefetching scheme. We make a relationship graph-based clustering to partition history user access sequences into several clusters and choose representative sequences from among these clusters as user access patterns. Then, these user access patterns are prioritized by their popularity and users’ personal preference. Based on these access patterns, the proposed prefetching scheme predicts the scene contents that will most likely be visited in the future and delivers them to the client in advance. The experiment results demonstrate that our user access pattern-based prefetching approach achieves a high hit ratio and outperforms the prevailing prefetching schemes in terms of access latency and cache capacity.

Algorithm for Mining Web Access Patterns Based on User Access Sequences

Through analysis of user access sequences, this research studies the browsing behaviors of web users and pro- poses a user access pattern mining algorithm based on the maximal forward reference sequence to determine frequent ac- cess paths. Experiments have shown that this effective algorithm could obtain a satisfactory result. This algorithm is supe- rior to the traditional Full Scan algorithm in terms of execution time and performance.

Application of Data Pre-Processing Method in Web Mining

With the widely application of the WWW and the emergence of Web technology, make the research of data mining has entered a new stage. Web log mining is based on the idea of data mining to analyze the server log processing. Paper aimed at the early stage of the data mining is put forward based on log data preprocessing methods, the purpose is to divide server logs into multiple unique user access sequence at a time, and to give a good algorithm.

Personalized recommendation with adaptive mixture of markov models

AbstractWith more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt‐ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web‐based knowledge management system, Livelink.

Efficient data mining for path traversal patterns

The authors explore a new data mining capability that involves mining path traversal patterns in a distributed information-providing environment where documents or objects are linked together to facilitate interactive access. The solution procedure consists of two steps. First, they derive an algorithm to convert the original sequence of log data into a set of maximal forward references. By doing so, one can filter out the effect of some backward references, which are mainly made for ease of traveling and concentrate on mining meaningful user access sequences. Second, they derive algorithms to determine the frequent traversal patterns-i.e., large reference sequences-from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences; one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed. It is shown that the option of selective scan is very advantageous and can lead to prominent performance improvement. Sensitivity analysis on various parameters is conducted.