Laxman Sahoo

Bio: Laxman Sahoo is an academic researcher from KIIT University. The author has contributed to research in topics: Cluster analysis & Big data. The author has an hindex of 4, co-authored 20 publications receiving 102 citations. Previous affiliations of Laxman Sahoo include Northern India Engineering College.

A Survey on Recommendation System

Abstract: Recommendation systems have become extremely common in recent years. It helps the customer to discover information and settle on choices where they do not have the required learning to judge a specific item. It can be utilized as a part of different diverse approaches to encourage its customer with effective information sorting. It is a software tool and techniques that provide suggestion based on the customer's taste to discover new appropriate thing for them by filtering personalized information based on the user's preferences from a large volume of information. Users taste and preferences should be constructed accurately in order to provide most relevant suggestions. This survey paper compare's and details the various type of recommender system and popular recommendation algorithms and its uses.

Personalized Movie Recommendation System Using Twitter Data

Abstract: Nowadays, we are living in an age recommendation, but the proper recommendation needs more accurate and relevant datas as their inputs. Rating databases like MovieLence or Netflix have long been popular and being widely used in recommendation system areas for research in past decades. But nowadays, they become irrelevant due to lack of new and relevant datas. Nowadays, social media like Facebook and Twitter become the most popular for researchers due to availability of large amount of new and relevant datas. In this paper, we have built a recommendation engine by analyzing rating datasets collected from Twitter to recommend movies to specific user using R.

An Effective Clustering Algorithm for Data Mining

Abstract: This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters The algorithm is distance-based and creates centroids To evaluate the proposed algorithm, we use some artificial data sets and compare with results of K-means Experimental results show that the proposed algorithm has better performance and efficiently finds accurate clusters

AKM—Augmentation of K-Means Clustering Algorithm for Big Data

Abstract: Clustering for big data analytics is a growing subject due to the large size of variety data sets needed to be analyzed in distributed and parallel environment. An augmentation of K-Means clustering algorithm is projected and evaluated here for MapReduce framework by using the concepts of genetic algorithm steps. Chromosome formation, fitness calculation, optimization, and crossover logics are used to overcome the problem of suboptimal solutions of K-Means clustering algorithm and reduction of time complexity of genetic K-Means algorithm for big data. Proposed algorithm is not dealing with the selection of parents to be sent to mating pool and mutation steps, so the performance time is improved.

Survey on Sentiment Analysis: A Comparative Study

Abstract: Sentiment Analysis (SA) is one of the greatest broadly planned applications of Natural Language Processing (NLP) and Machine Learning (ML). This field has grown enormously with the advent of the Web 2.0. The Internet has as long as a platform for people to express their opinions, emotions and feelings towards products, persons, and life in general. Accordingly, the Internet is nowadays a massive resource of opinion amusing written data. A vital job of sentiment analysis is sentiment classification, which intentions to automatically classify opinionated text as being negative, positive, or neutral. This paper provides a comparative study on sentimental analysis and its applications mostly for recommendation system. Recommender systems have grown to be a serious research area after the emergence of the first paper on collaborative filtering in the Nineties.

Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

Abstract: Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, the environments are commonly dynamic, in which the performance of reinforcement learning approaches can degrade drastically. A direct cause of the performance degradation is the high-variance and biased estimation of the reward, due to the distribution shifting in dynamic environments. In this paper, we propose two techniques to alleviate the unstable reward estimation problem in dynamic environments, the stratified sampling replay strategy and the approximate regretted reward, which address the problem from the sample aspect and the reward aspect, respectively. Integrating the two techniques with Double DQN, we propose the Robust DQN method. We apply Robust DQN in the tip recommendation system in Taobao online retail trading platform. We firstly disclose the highly dynamic property of the recommendation application. We then carried out online A/B test to examine Robust DQN. The results show that Robust DQN can effectively stabilize the value estimation and, therefore, improves the performance in this real-world dynamic environment.

Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor

Abstract: In the field of Artificial Intelligence Machine learning provides the automatic systems which learn and improve itself from experience without being explicitly programmed. In this research work a movie recommender system is built using the K-Means Clustering and K-Nearest Neighbor algorithms. The movielens dataset is taken from kaggle. The system is implemented in python programming language. The proposed work deals with the introduction of various concepts related to machine learning and recommendation system. In this work, various tools and techniques have been used to build recommender systems. Various algorithms such as K-Means Clustering, KNN, Collaborative Filtering, Content-Based Filtering have been described in detail. Further, after studying different types of machine learning algorithms, there is a clear picture of where to apply which algorithm in different areas of industries such as recommender systems, e-commerce, etc. Then there is an illustration of how implementations and working of the proposed system are used for the implementation of the movie recommender system. Various building blocks of the proposed system such as Architecture, Process Flow, Pseudo Code, Implementation and Working of the System is described in detail. Finally, in this work for different cluster values, different values of Root Mean Squared Error are obtained. In this proposed work as the no of clusters decreases, the value of RMSE also decreases. The best value of RMSE obtained is 1.081648. The results given by the proposed system are better than the existing technique on the basis of RMSE value.

Recommender Systems Challenges and Solutions Survey

Abstract: Today’s Recommender system is a relatively new area of research in machine learning. The recommender system’s main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user – content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user’s items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.