Analyzing the effectiveness of collaborative filtering and content-based filtering methods in anime recommendation systems

In the current digital era where content consumption via streaming platforms is increasing, the need for accurate recommendation systems is becoming increasingly important, especially in the animation industry. This research focuses on implementing a recommendation system that can help viewers easily navigate the abundance of content. By comparing collaborative filtering and content-based filtering methods, this research attempts to find the optimal approach for providing anime recommendations. From the results of A/B testing and further analysis, it was found that Collaborative Filtering was effective in providing recommendations based on similar interests between users. On the other hand, content-based filtering offers the advantage of personalizing recommendations based on content characteristics. Additionally, integrating these techniques into mobile applications will enrich the user experience, allowing them to receive recommendations more quickly and interactively. With these findings, this research contributes to the development of more intuitive and responsive recommendation systems, driving the growth of the anime streaming industry by increasing user satisfaction and retention

ck-NN: A Clustered k-Nearest Neighbours Approach for Large-Scale Classification

k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow

A novel ontology framework supporting model-based tourism recommender

In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework. © 2021, Institute of Advanced Engineering and Science. All rights reserved

An integrated recommender system for improved accuracy and aggregate diversity

Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers

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