Volume 7, Issue 5, May 2018


Personalized Micro Blog Recommendation Using Sentimental Features

Authors: Priyanka Buwade, Prof .Vivek Kumar

Abstract- Recently, microblogs have emerged as a new open channel of communication for people on the Internet to read, commentate, socialize and so on. With the advent of a huge number of information in microblog spaces, including articles, profile, pictures and other multimedia resources, the “information overload” has become a critical problem for microblog users, which brings bloggers plethora of choices and options available that often varies in quality and may severely affected the recommendation quality. Accordingly, providing microblog users with articles that suit their particular preferences is an important issue. To solve this problem, this paper proposes a novel method integrating social network information and collaborative filtering. A user ranking model based on social network analysis is constructed to estimate the correlations between microblog users, and incorporated into similarity measure for improving the quality of microblog recommendation. Experiments on a realworld dataset are carried out to evaluate the performance of the presented method. The results show that the proposed method outperforms traditional KNN method and improves recommendation quality effectively.

Keywords- Microblog Recommendation, Similarity Measurement, Social Network


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Frequent Item Set using Apriori and Map Reduce Algorithm: An Application in Inventory Management

Authors: Kranti Patil, Jayashree Fegade, Diksha Chiramade, Srujan Patil, Pradnya A. Vikhar

Abstract- Data mining (DM) is a computerized technology that uses complicated algorithms to find relationships in large data bases Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. Association Rules Mining (ARM) is a function of DM research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. It is a universal technique which uses to refine the mining techniques. In computer science and data mining, Apriori is a classic algorithm for learning association rules Apriori algorithm has been vital algorithm in association rule mining. Apriori algorithm is a realization of frequent pattern matching based on support and confidence measures produced excellent results in various fields. Main idea of this algorithm is to find useful patterns between different set of data. It is a simple algorithm yet having many drawbacks. So Apriori based MapReduce algorithm is proposed. Thus, there have been many approaches to convert many sequential algorithms to the corresponding Map/Reduce algorithms. Thus we presents Map/Reduce algorithm of the legacy Apriori algorithm that has been popular to collect the item sets frequently occurred in order to compose Association Rule in Data Mining. Theoretically, it shows that this algorithm provides high performance computing depending on the number of Map and Reduce nodes. The used Apriori based MapReduce algorithm will help in reducing multiple scans over the databases by cutting down unwanted transaction records for finding frequent itemsets.

Keywords- Map/Reduce, Apriori algorithm, Data Mining, Association Rule


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