User – Centric Similarity Search

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User-Centric Similarity Search

User – Centric Similarity Search

Abstract

User – Centric Similarity Search java project report User preferences play a significant role in market analysis. In the database literature there has been extensive work on query primitives, such as the well known top-k query that can be used for the ranking of products based on the preferences customers have expressed.

Still, the fundamental operation that evaluates the similarity between products is typically done ignoring these preferences. Instead products are depicted in a feature space based on their attributes and similarity is computed via traditional distance metrics on that space. In this work we utilize the rankings of the products based on the opinions of their customers in order to map the products in a user – centric space where similarity calculations are performed.

System Configuration:

H/W System Configuration:-

System             : I3 Processor.

Hard Disk          : 500 GB.

Monitor             : 15’’ LED

Input Devices    : Keyboard, Mouse

Ram                 : 4 GB

S/W System Configuration:-

Operating system    : Windows 7/UBUNTU.

Coding Language     : Java 1.7 ,Hadoop 0.8.1

IDE                        : Eclipse

Database                : MYSQL

Conclusion

In User – Centric Similarity Search java project report paper we introduced a user – centric similarity framework in which the similarity of products is assessed by taking into account user preferences. We demonstrated via examples and through our experiments that user – centric similarity search can yield quite different results than using conventional metrics that only look at the products, in isolation to the preferences their customers have expressed. We identified two interesting query types and we proposed efficient algorithms for their execution. We also discussed optimizations that help reduce execution times.

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Name of the Project   : User – Centric Similarity Search

Project Cost                : $ 50

Delivery Time             :  Within 48 hours

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