A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems

Horizontal Class Fragmentation in Distributed Object Based Systems

Many researchers have demonstrated the importance of entity fragmentation in distributed relational database design. Database design will be essential in the "next-generation" engineering design environment that exploits object-oriented technologies. Fragmentation enhances application performance by reducing the amount of irrelevant data accessed and the amount of data transferred unnecessarily between distributed sites. Algorithms for effecting horizontal and vertical fragmentation of relations exist, but fragmentation techniques for class objects in a distributed object based system have not appeared in the literature. This paper first presents a taxonomy of the fragmentation problem in a distributed object based system capable of supporting systems engineering applications. Detailed horizontal fragmentation algorithms are then presented for one of these class models using a top--down approach where the entity of fragmentation is the class object. The algorithms described i..

Community Opinion Network Maximization for Mining Top K Seed Social Network Users

The Active Community Opinion Network Mining and Maximization (ACOMax) system being proposed improves on the capabilities of an existing system, Opinion Based Influence Network (OBIN) system by integrating active opinion mining (OM) as in the system, aCtive OpinioN Estimator (CONE). This work also adds opinion maximization to enhance OBIN’s accuracy while using joint aCtive OpinioN Estimator (CONE’s) training time to solve OBIN’s cold-start problem. It does this using joint sentence-level and feature-level opinion mining of mined community opinions for filtering out non-positive opinions. ACOMax first mines multiple posts related to a product using Twitter API, before performing joint opinion mining on selected posts from these user reviews. From the selected posts, ACOMax obtains frequent features with favorable opinions of the product, which it uses to construct a community opinion network graph of users sharing positive opinions. The graph is used by the seller to actively find the top k seed users to produce maximum opinion spread when Multiple Linear Threshold (MLT) is used for opinion maximization. The proposed system improves total opinion spread in a social network over the compared systems. When a small top k = 10 seed users is used, the experimental results show a total opinion spread of 263 users for ACOMax system in comparison to the 112 users produced by the system CONE for a 134 % improvement in opinion spread. ACOMax also improves on OBIN system’s accuracy as while it achieves respective higher scores of 98.19 %, 98.50 %, and 97.89 % for F1, precision and recall; OBIN system achieves 95.3 %, 98.24 %, and 93.71 %

LSC-Mine: Algorithm for Mining Local Outliers

Data objects which differ significantly from the remaining data objects are referred to as outliers. Density-based algorithms for mining outliers are very effective in detecting all forms of outliers, where data objects with fewer neighbors are likely to be outliers than are those with more neighbors. However, existing density-based algorithms engage in huge repetitive computation and comparison for every object before the few outliers are detected. Expensive computations might make scalability of these techniques to important applications like quick fraud detection unfeasible. This paper proposes LSC-Mine algorithm based on the distance of an object and those of its knearest neighbors. In addition, data objects that are not possible outlier candidates are pruned which reduces the number of computations and comparisons in LOF technique resulting in an improved performance. 1

Mining Incremental Association Rules with Generalized FP-Tree

Abstract. New transaction insertions and old transaction deletions may lead to previously generated association rules no longer being interesting, and new interestingassociation rules may also appear. Existingassociation rules maintenance algorithms are Apriori-like, which mostly need to scan the entire database several times in order to update the previously computed frequent or large itemsets, and in particular, when some previous small itemsets become large in the updated database. This paper presents two new algorithms that use the frequent patterns tree (FP-tree) structure to reduce the required number of database scans. One proposed algorithm is the DB-tree algorithm, which stores all the database information in an FP-tree structure and requires no re-scan of the original database for all update cases. The second algorithm is the PotFp-tree (Potential frequent pattern) algorithm, which uses a prediction of future possible frequent itemsets to reduce the number of times the original database needs to be scanned when previous small itemsets become large after database update. Keywords: Incremental maintenance, association rules mining, FP-tree structure

The HSPRec E-Commerce System Open Source Code Implementation

To promote big data application access, usage and deployment, this paper presents a downloadable open source code implementation for an E-Commerce Recommendation system, HSPRec (Historical Sequential Pattern Recommendation System), in JAVA. The HSPRec system is composed of six different modules for generating purchase/click sequential databases, mining sequential patterns, computing click purchase similarities, generating purchase sequential rules, computing weights for frequent purchase patterns through Weighted Frequent Purchase Pattern Miner, and normalization of the user-item ratings to predict level of interest. The source code of each module and the main runner are discussed under four possible headings of running environment, input data files and format, minimum support format, output data files and format. The overall goal of the HSPRec system is to improve E-commerce Recommendation accuracy by incorporating more complex sequential patterns of user purchase and click stream behavior learned through frequent sequential purchase patterns. HSPRec provides more accurate recommendations than the tested comparative systems