On both Cold-Start and Long-Tail Recommendation with Social Data

On both Cold-Start and Long-Tail Recommendation with Social Data

₹4,000.00
Product Code: Java - Big data
Availability: In Stock
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Product Description

Aim

           The main aim of this project is to achieve the cold start and long tail problems and recommend the products based on user previous transactions data.


Synopsis

              In web system number of people visiting the website like ecommerce, adversting system and multimedia consumption system are important for the website holder. website holders has to make a note on how many people visiting their website  it can be calculated to find the total hits of the visitors based on that product will be recommended  There is one  problem if the website didn’t show any product to the new customer user cannot able to see the product sometime new user may visit the website and some irrelevant product will be shown. Recommender system plays a important role of discovering interesting items from near-infinite inventory and exhibiting them to potential users. Yet, two problems are incapable in  the recommender systems. One is “how to handle new users”, and the other is “how to surprise users”. The former is well-known as cold-start recommendation and latter shown as long tail recommendation

    

Proposed System :

    
            Based on the problems due to cold start and long tail, products in the ecommerce retailers getting sold out so soon . And some kind of products remains stagnant for a long duration. Thus to overcome  these problems we are going to track the selling of products as well as recommending the products to the customers of same kind based on the previous purchases by using clustering and classification we achieve this First user register the details like name ,password,address and registers then  login with valid credentials then the large amount of transactional data collected from customer for segmentation and classification and these data are preprocessed and cluster and classify the data cold product classified based on sale and long tail is classified based product that getting sold frequently after that cold products that are available in the ecommerce site will be recommended to the user to make retailer some profit for the product and finally long tail problem solved by recommending relevant item and product that are not sold for long period gets attractive offer based on the user request.We use hadoop environment to do manipulation with the products dataset.

 

Advantages:


          Attractive offers are promoted to the user.
          Recommends products to attain margin of retailers. Retailer achieves profit for the cold products
          Recommends other products that have same features of long tail products.
          Cold stat and longtail problem handled and to make sales possible.

 

Algorithm:

       
          Random  Forest Algorithm

Software Requirements:

            -    Windows 7

            -    JDK 1.7

            -    J2EE

            -   Tomcat 8.5

            -    MySQL

            -    Hadoop

Hardware Requirements:

                Hard Disk :           250GB and Above

                RAM         :           4GB and Above

                Processor :           I3 and Above(64 bit)

Technologies Used:        

                -    J2EE (Jsp, Servlets)

                -    Struts 2.0 Framework

                -    JavaScript , Ajax , HTML ,CSS

                -    Webservices (JAX -ws)

                -    Hadoop 2.3

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The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

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  3. Base paper
  4. Full Project PPT
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The Delivery time for Hardware projects is 7-8 working days.

   

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The Delivery time for Hardware Mini projects is 7-8 working days.