• Corpus ID: 20098504

Similarity Measures used in Recommender Systems : A Study

  title={Similarity Measures used in Recommender Systems : A Study},
  author={Ajay Agarwal and Minakshi Chauhan and Ghaziabad},
Information is growing exponentially over the Internet. User gets confused while seeing so many items over the Internet to decide which one to buy. In this scenario filtering of available information is essential to suggest user about items and tell what other users recommend. One user will set his mind to buy only if many like-minded users like a particular item. To get the group of similar users or items the vendor has to find similarity among them by quantifying their recommendations. This… 

Figures and Tables from this paper

The objective of this paper is to identify appropriate distance measures for datasets and furthermore to facilitate comparison and assessment of the proposed similarity measures with that of conventional ones.
An Enhanced Similarity Measure for Collaborative Filtering-based Recommender Systems
This work proposes a new method to compute the similarity between two users/items to overcome the shortcomings of the existing measures, and thereby improve the accuracy of prediction in the CF-based RSs.
A Book Recommender System Using Collaborative Filtering Method
The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users, which showed better performance than the matrix factorization method with respect to fitting and testing time.
A Lexicon-based Collaborative Filtering Approach for Recommendation Systems
The proposed lexicon-based k nearest neighbors collaborative filtering technique replaces the numerical rating with a computed sentiment rating in the neighborhood determination step and produces reliable values in terms of mean absolute error and root mean square error and accurate recommendations for users.
A new way of finding better neighbors in recommendation systems based on collaborative filtering
A model that finds the closest neighbors efficiently incorporating dimensionality reduction, using Truncated Singular Value Decomposition which helps with sparse data and avoids noise caused by lack of ratings, then using clustering as the authors have a dense reduced matrix, and finally applying the correct similarity metric to improve predictions.
A Sentiment-based Similarity Model for Recommendation Systems
  • Mara Deac-Petrusel, Sergiu Limboi
  • Computer Science
    2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
  • 2020
The goal of the proposed paper is to exploit the valuable information offered by the textual reviews, by mixing Sentiment Analysis techniques into the recommendation process, by developing a sentiment rating approach based on calculated sentiment scores for each item.
Comparison of Generic Similarity Measures in E-learning Content Recommender System in Cold-Start Condition
A comparative study of 4 generic similarity measures that are widely used in e-learning recommender systems and results indicate better recommendation performance when using Cosine Vector Similarity in cold-start condition.
Implementation of Popular Techniques for Movie Recommendations
This study discusses and analyses the various approaches and techniques used for the recommendation of movies and reveals some techniques which reflect the current trend and popularity of movies.
Similarity Measure for Product Attribute Estimation
A method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception, which has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE).


A collaborative filtering similarity measure based on singularities
Evaluation of Item-Based Top-N Recommendation Algorithms
The experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.
Item-based collaborative filtering recommendation algorithms
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
It is shown that using weighted similarity measures significantly improves the results of both user- and item-based approaches, and bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results.
Item-based top-N recommendation algorithms
This article presents one class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended, and shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
A collaborative filtering approach to mitigate the new user cold start problem