Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity

Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures

Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model\u27s complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users\u27 history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system’s performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top state-of-the-art performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model

A new curve fitting based rating prediction algorithm for recommender systems

summary:The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is $\theta(n^2)$, for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms

Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization

Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods

Exploiting Intrinsic Clustering Structure in Discrete-Valued Data Sets for Efficient Knowledge Discovery in the Presence of Missing Data

Scalable algorithm design has become central in the era of large-scale data analysis. The vast amounts of data pouring in from a diverse set of application domains, such as bioinformatics, recommender systems, sensor systems, and social networks, cannot be analyzed efficiently using many data mining and statistical tools that were designed for a small scale setting. It is an ongoing challenge to the data mining, machine learning, and statistics communities to design new methods for efficient data analysis. Confounding this challenge is the noisy and incomplete nature of real-world data sets. Research scientists as well as practitioners in industry need to find meaningful patterns in data with missing value rates often as high as 99%, in addition to errors in the data that can obstruct accurate analyses. My contribution to this line of research is the design of new algorithms for scalable clustering, data reduction, and similarity evaluation by exploiting inherent clustering structure in the input data to overcome the challenges of significant amounts of missing entries. I demonstrate that, by focusing on underlying clustering properties of the data, we can improve the efficiency of several data analysis methods on sparse, discrete-valued data sets. I highlight new methods that I have developed with my collaborators for three diverse knowledge discovery tasks: (1) clustering genetic markers into linkage groups, (2) reducing large-scale genetic data to a much smaller, more accurate representative data set, and (3) computing similarity between users in recommender systems. In each case, I point out how the underlying clustering structure can be used to design more efficient algorithms, even when high missing value rates are present