lars an efficient and scalable location-aware recommender system

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 6, JUNE 2014 LARS : An Efficient and Scalable Location-Aware Recommender System

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Page 1: LARS An Efficient and Scalable Location-Aware Recommender System

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 6, JUNE 2014

LARS : An Efficient and Scalable Location-Aware Recommender System

Page 2: LARS An Efficient and Scalable Location-Aware Recommender System

Abstract

In the latest trend of internet, optimized suggestions for the search are anticipated by every individual.

Crowd sourcing, a largest human resource provider network useful in search suggestions that helps to find out common phrases that other people have searched for.

We identify an interesting real time problem, finding the best products through suggestions with individual user rating to a particular brand as well as with the rating of the friends to the product and the generalized crowd sourcing opinion.

The system comprise of product to product co-relation under a brand, user to user co-relation under common attributes, crowd sourcing opinion by means of key factors obtained for the product in order make the suggestions more optimal.

Page 3: LARS An Efficient and Scalable Location-Aware Recommender System

Existing System

In the existing system, the concepts of mining is predominant to match the preference and tastes of one user with other in order to make suggestions for the search.

Generalised rating of the product has been gathered in the existing system to suggest any user according to the mining strategies.

There is no specific consideration of rating in order to suggest the user’s search that could serve as an optimal suggestion for the search.

The good rating by an user for a particular product of a brand that owns many product may not be considered to suggest the same user if he/she searches for some other product that the brand itself owns

Page 4: LARS An Efficient and Scalable Location-Aware Recommender System

Proposed System

A novel method of mining has been concentrated in the system so as to thrive the suggestion engine more effective for every individual users.

This system efficiently overcome the drawbacks of the existing system by considering the factors of product to product co-relation and users to users co-relation and key factors obtained from crowd sourcing data.

Rating for the product has been considered specifically according to each user’s individual brand opinion about the product, opinion of the user’s friend about the product and the crowd sourcing opinion.

According to the rating of each of these mentioned factors an optimal suggestion for the search has been made to the user

Page 5: LARS An Efficient and Scalable Location-Aware Recommender System

Architecture Diagram

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