A novel, generalized recommender system for social media using the collaborative-filtering technique

2014 ◽  
Vol 39 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Ramesh A. ◽  
Anusha J. ◽  
Clarence J.M. Tauro
Keyword(s):  
Social Media ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Filtering Technique

scholarly journals A Research on Similarity Measure to Identify Effective Similar Users in Recommender Systems

2019 ◽  
Vol 8 (9S2) ◽  
pp. 834-840
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Drastic Increase ◽  
Measure Model ◽  
Filtering Technique ◽  
Selection Of

In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user

scholarly journals THE RECOMMENDATION ALGORITHM FOR AN ONLINE ART GALLERY

2018 ◽  
Vol 7 (2) ◽  
pp. 108-119
Author(s):  
Waldemar Karwowski ◽  
Marian Rusek ◽  
Joanna Sosnowska
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation Systems ◽  
User Profile ◽  
Art Gallery ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Filtering Technique

The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.

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 Recommender system for surplus stock clearance

2019 ◽  
Vol 9 (5) ◽  
pp. 3813
Author(s):  
Vipul Agarwal ◽  
Vijayalakshmi A
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
Similarity Metrics ◽  
Manual Work ◽  
Similarity Metric ◽  
Filtering Technique ◽  
Retail Shop ◽  
Item Item

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

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