Enhancement of Resource Recommendation System with Graph Based Using som sandeep Pradhan Mr. Sandeep Gonnade

Computer Sc. & Eng Asst. Professor, Computer Sc. & Engg.

MATS University MATS University

Raipur, India Raipur, India

Sandeeppradhan22@gmail.com sandeepg@matsuniversity.ac.in

Abstract: - This is a paper about recommender system, and the specific techniques that recommendation system uses to remedy the Information overload problem. Its center of attention lies on two most common approaches they are collaborative filtering and content based filtering. Both of these methods have their weaknesses and strengths. To beat the weaknesses of each and every process, quite a lot of hybrid filters had been developed. It will concentrate on Kohonen’s self-organizing map algorithm, used to identify the neighborhood of the current user so that relevant resources are suggested. The implementation of this algorithm shall be used alongside collaborative filtering process in the effort of designing a recommender system for books. A graph based recommender system makes useful recommendations by exploiting the significant content available. For calculating the ratings of the resources a weight based approach is used. This system is known as a web based client side application which makes recommendations by constructing user-resource graph and ranking the resources by a new method designed similar to that of search algorithms.

Keywords: - Recommendation system, graph based recommender system, Neighborhood, Weight based and Self-Organizing map.

1. 
   INTRODUCTION
   

The most common techniques used in today’s recommendation systems fall into two distinct categories: content-based methods and collaborative filtering methods [4,5]. And collaborative filtering has been known to be the most successful recommendation techniques. Collaborative methods recommend items based on aggregated user ratings of those items and these techniques don’t rely on the availability of textual descriptions. They share the common goal of assisting in the user’s search for items of interest, and thus attempt to address one of the key research problems in the area of the information age: locating needles in a haystack that is growing exponentially. Collaborative filtering systems can deal with massive number of individual and with many alternative items. However there’s a problem that the set of ratings is sparse, such that any two users will most likely have only a few co-rated items. The high dimensional sparsity of the user-item rating matrix and the problem of scalability result in low quality recommendations.

The structured of the paper is as follows: Section 2 focuses on the various issues faced by recommender systems. Section 3 describes the proposed work. Sections 4 describe our system model and its various modules. Section 5 depicts the results that we got after experimentation with data and also the efficiency measure based upon performance metrics. Our work is concluded in section 6.

2. 
   ISSUES FACED BY RECOMMENDER SYSTEMS
   

Collaborative filtering recommender systems to have their own share of issues, related to data sparsity [11] are the most prominent one. It’s highly likely that a user would rate solely a few items in an item domain and thus the extremely sparse user-item ratings matrix would cause to a downgraded performance.

We proposed a new hybrid system which combines the techniques used in graph based resource recommendation system. The proposed system use the ratings of resources which is provided by graph based resource recommendation system [1] and by applying self-organizing map algorithm we can easily make clusters of similar resources by reducing the redundancy and data sparsity.

1. 
   Assign random values to weight vector of neurons and determine the topology of the SOM.
   
2. 
   Initialize the weight vector V_i(0) for i=1,2,…….k randomly.
   
3. 
   Provide an input vector to the network.
   
4. 
   Traverse each node in the map
   

• 
  Use the Euclidean distance formula to find the similarity between the input vector and the map's node's weight vector
  
• 
  Track the node that produces the smallest distance (this node is the best matching unit, BMU)
  

5. 
   Update the nodes in the neighborhood of the BMU (including the BMU itself) by pulling them closer to the input vector
   

Where

V_i denotes the current weight vector.

D denotes the target input.

α(t) denotes learning rate due to time.

6. Increment t and repeat from step 2 while t >

λ. Where λ is the limit on time iteration

Self organizing feature maps (SOFM) also known as Kohonen feature maps belong to the class of competitive learning based cluster. The nodes (neurons) of these become specifically tuned to varied input patterns. It consists of two layers of neurons, an input layer and a so-called competition layer. When an input pattern is presented to the network, that vegetative cell within the competition layer is determined, the reference vector of that is nearest to the input pattern. This neuron is termed the winner neuron. The learning is such just one node (neuron) at the output becomes active corresponding to a specific input pattern. the learning in SOFM, however, differs from the other competitive based.

n_c (t1)

n_c (t2)

n_c (t3)

n_c (t4)

Fig.2 Topological Neighborhood of SOM net

clustering methods in that not only the weight of the winning neuron is going to change but also those in its neighborhood N_c (t) (with t₁ < t₂< t₃ < t₄) which is defined in terms of some proximity relation. This neighborhood relation is usually represented as a (usually two-dimensional) grid as shown in Fig:2, the vertices of which are the neurons. The grid is most often rectangular or hexagonal.

3. 
   IMPLEMENTATION
   

The steps for recommending books to the user are explained below.

• 
  Step1: From the user input, the profile based on his preferences and age is built.
  
• 
  Step2: Generate the cluster by using SOM algorithm.
  
• 
  Step3: Find the cluster in which the user is present by calculating the Euclidean Distance with each centroid and assign the centroid with the minimum distance to be the neighborhood.
  
• 
  Step4: Identify the books which are more popular in the neighborhood. Calculate the ratings for the resources.
  
• 
  Step5: Construct the user–book graph and recommend the books based on the ranking method.
  

4. 
   RESULTS AND DISCUSSION
   

K-means, K-medoids, FCM and SOM clustering algorithm are compared by plotting a graph which is shown in Fig.3. The graph shows that by using SOM algorithm the number of relevant books recommended are more than other clustering technique. From the graph it is evident that using the SOM clustering algorithm does actually increase the correctness in recommendations. Xaxis consists of different genres of books while Yaxis consists of number of resources.

5. 
   CONCLUSION
   

Which provide more accurate prediction to the users.

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