IMPROVING RECOMMENDATION PERFORMANCE WITH USER INTEREST EVOLUTION PATTERNS

Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static information of customers’ preferences and ignore the evolution of their purchasing behaviours which contain valuable information for making recommendations. Thus, this study proposes an approach to increase the effectiveness of personalized recommendations by mining the sequence patterns from the evolving preferences of a target customer over time. The experimental results have shown that the proposed technique has improved the recommendation precision in comparison with collaborative filtering method based on Top k recommendation

Nomenclature and Contemporary Affirmation of the Unsupervised Learning in Text and Document Mining

Document clustering is primarily a method applied for an uncomplicated, document search, analysis and review of content or is a process of automatic classification of documents of similar type categorized to relevant clusters, in a clustering hierarchy. In this paper a review of the related work in the field of document clustering from the simple techniques of word and phrase to the present complex techniques of statistical analysis, machine learning etc are illustrated with their implications for future research work

A two-level Product Recommender for E-commerce Sites by Using Sequential Pattern Analysis

With the development of communication networks and rapid growth of their applications, huge amount of information have been produced. Major part of these information are in electronic stores, and hence it's really hard to find desired products inside huggermugger. Product Recommendation System (PRS) tries to solve this problem by giving appropriate and fast recommendations to the customers. This paper proposes a two-level product recommender for E-commerce sites. At first, the available products are clustered by using C-Means algorithm to create groups of products with similar characteristics. Then, the second level considers the customers’ behavior and their purchase history for drawing the relationships between products by using Sequential Pattern Analysis (SPA) method. These relationships, eventually, will lead to appropriate recommendation for customers and also increases the likelihood of selling related products in electronic transactions. Extensive numerical simulations over UCI transactions 10k dataset indicates that 87% of records in mined sequential patterns are predicted correctly and the accuracy of recommendations is more than other RPSs

Recurrent Session Approach to Generative Association Rule based Recommendation

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations.  The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot.  In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability

Game analytics - maximizing the value of player data

During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.peer-reviewe