scholarly journals User-Review Oriented Social Recommender System for Event Planning

2020 ◽  
Vol 25 (5) ◽  
pp. 669-675
Author(s):  
Rahul Kumar Singh ◽  
Pardeep Singh ◽  
Gourav Bathla
Keyword(s):  
Recommender System ◽  
User Satisfaction ◽  
Recommendation System ◽  
Hybrid Approach ◽  
Social Commerce ◽  
System Framework ◽  
User Comments ◽  
Filtering Technique ◽  
Event Based ◽  
User Review

Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

A Prospect on an Intelligent Recommender System

2021 ◽  
Vol 12 (2) ◽  
pp. 25-43
Author(s):  
Pooja ◽  
Vishal Bhatnagar
Keyword(s):  
Recommender System ◽  
User Satisfaction ◽  
Recommendation System ◽  
Intelligent System ◽  
Literature Survey ◽  
Research Papers ◽  
Online Data ◽  
Principle Component ◽  
The Future

User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies the areas of the future for the development of an intelligent system.

Personalized Recommender System for Digital Libraries

2014 ◽  
Vol 9 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Omisore M. O. ◽  
Samuel O. W.
Keyword(s):  
Recommender System ◽  
Digital Libraries ◽  
User Satisfaction ◽  
Information Filtering ◽  
Cold Start ◽  
Web Based ◽  
Reading Abilities ◽  
Filtering Technique ◽  
Personalized Recommender System ◽  
Matching Techniques

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.

scholarly journals Techniques of Recommender System

2019 ◽  
Vol 8 (9S) ◽  
pp. 373-379
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Information Overload ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Data Filtering ◽  
Filtering Technique ◽  
Comprehensive Introduction ◽  
Hybrid Recommendation ◽  
Content Based Filtering

The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

scholarly journals Top-k Recommended Items: Applying Clustering Technique for Recommendation

2019 ◽  
Vol 12 (2) ◽  
pp. 106-117
Author(s):  
Kittisak Onuean ◽  
Sunantha Sodsee ◽  
Phayung Meesad
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Recommendation System ◽  
Experimental Results ◽  
User Preference ◽  
Data Sparsity ◽  
Clustering Techniques ◽  
Item List ◽  
Filtering Technique ◽  
Item Types

This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference.  In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction and Recommend. Simple clustering was used to create a system to provide suggestions for sparsity data. Datasets obtained from clustering represented the sample agent of dataset to being create the recommendation system. Datasets were divided into two categories, 1) Traditional Data (TD) and 2) Statistic Data (SD), and each dataset clustered by k-means clustering. The experimental results demonstrated that the number of item types in the system were recommended in the TD and Euclidean (DIS). DIS was used to find the nearest value in TD for the item list recommendation to active users in the system with the a lot of number choice of recommendation system.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

2016 ◽  
Vol 16 (6) ◽  
pp. 245-255 ◽  
Author(s):  
Li Xie ◽  
Wenbo Zhou ◽  
Yaosen Li
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Recommendation System ◽  
Parallel Implementation ◽  
Implementation Process ◽  
Recommendation Algorithm ◽  
Filtration Problem ◽  
Information Filtration ◽  
Similarity Information ◽  
Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

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