Advanced Analytics and Deep Learning Models

Figure 3.5 Experimental result.

In this research activity, they suggested a clustering method to use multiple-criteria rating into conventional recommendation system successfully. To generate more on the mark recommendations, they evaluate the intra-cluster client matches by applying Mahalanobis distance approach. Then, they collated their method with the conventional CF [2].

Now, we will take a look on their experimental evaluation and result for its efficiency.

To implement this proposed approach, they have used Yahoo! Movies dataset. This dataset consists of 62,156 rating provides by 6,078 users on 976 movies. To make it simple, they have extracted those clients that have rated to minimum 20 movies. This condition satisfies 484 users and 945 movies, and they have total 19,050 ratings. Then, every client’s rating is splitted arbitrarily into training and testing set. They took 70% of data for training purpose and remaining 30% of data for testing purpose. Then, they calculate the distance between clients successfully. They selected top 30 most equivalent users for the neighborhood set formation. To evaluate this proposed approach, they used the most popular Mean Absolute Error (MAE) performance matrix. MAE is very popular because of its simplicity and accuracy as we have seen before. It matches the goal of the experiment. The mean absolute error estimates the derivation of actual and predicted client ratings [2].

The dataset which they have used contain both single-criteria and multiple-criteria user provided ratings. Table 3.4represents the difference between their way with the conventional approach for both non-clustering and clustering environment. Here, they have compared their experimental result with Pearson collaborative recommender (PCRS) approach. Table 3.4conveys the result between PCRS with their proposed Mahalanobis distance recommendation scheme (MDRS). It is very transparent that their method MDRS performed much superior than traditional PCRS. These results are based on mean absolute error method. This proposed MDRS approach works better for both on clustering and clustering environments which is shown in Figure 3.6[2].

They also have shown the graphical implementation of this table which is in both non-clustering and clustering environments. The Mahalanobis distance–based method gives better result than the Pearson collaborative recommender approach. If the mean absolute error values are lower, then it means it is a better result. By that result, we can also see that the non-clustering–based technique always have less performance than clustering approaches. So, the clustering approach is better [2].

Table 3.4 Comparison among clustering and non-clustering approach.

Figure 3.6 Result.

They have consolidated the multiple-criteria ratings into the conventional CF-based recommender system using K-means algorithm. Their method treats the third dimensional as multi-criteria, the clustering parameters as the clustering parameter of the clients, to handle the dimensionality. Their approach depends on thinking like each user has unique opinion and criteria. Therefore, to compare each user, the most important concern of this work is to find out clients’ segments with alike client. Mahalanobis distance method is used here to create most exact neighbors for every client within the cluster. In the result, we can clearly see that this technique is more effective and accurate than the traditional approach [2].

Multi-criteria recommender system is used to make user experience easier or make things easy for each and every user. If we take a look on business perspective, then this system is used to grow a business.

Table 3.5 Comparison among existing methods in MCRS.

In business point of view, if a customer comes in any platform, then the main work of the owner and the staff is to satisfy the customer needs. They will make sure that the customer must have a good experience and got their desired thing. This is the main work of a recommender system. For this particular reason, many companies are adopting this multi-criteria recommender system [33].

Here are some benefits in businesses perspective that can be achieved by using MCRS:

MCRS has a big role in generating the revenue. Years of research, execution, and experiments, many researchers from all over the world made various types of recommender systems. Many different types of algorithms are executed and explored to get good accuracy. When a customer came to buy a particular thing then the MCRS shows him some similar things and accessories so that the customer will buy that too. This is one of the job of MCRS and also one of the effective ways to generate better revenue.