User Based Collaborative Filtering Using Bloom Filter with MapReduce

2020 ◽

Vol 9 (1) ◽

pp. 1741-1743

Keyword(s):

Collaborative Filtering ◽

Input Data ◽

Science And Technology ◽

Bloom Filter ◽

Foreign Student ◽

Phase Classification ◽

Filtering Technique ◽

Good Manner ◽

Input Phase ◽

Three Phases

This paper presents an approach of bloom filter classifier and collaborative filtering to help foreign student to choose the suitable faculty according to his nationality and number of years that need to study. Our approach consist of three phases are: input phase, classification phase, and recommendation phase. In Input phase, the student enters the nationality and number of suggested years study. In classification phase, the approach classifies the student according to input data based on bloom filter classifier. In recommendation phase, the approach recommended the top five faculty if exists based on collaborative filtering technique (CF). Our dataset collected from Misr University for Science and Technology (MUST) and the results of our approach suitable and has a good manner for the student with accuracy 90%.

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Improved Collaborative Filtering Recommendation Algorithm Based on Weibo Content

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188905 ◽

2020 ◽

Author(s):

Juntao Xue ◽

Ruohan Ma ◽

Yunfeng Zhao ◽

Junjie Hei

Keyword(s):

Collaborative Filtering ◽

Recommendation Algorithm

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Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.400 ◽

2017 ◽

Vol 7 (10) ◽

pp. 52

Author(s):

LAKSHMI PRANEETHA

Keyword(s):

Real Time ◽

Data Streams ◽

Bloom Filter ◽

Scientific Applications ◽

Pruning Algorithm ◽

Density Data ◽

Data Points ◽

Short Time ◽

Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

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