A new user similarity measure in a new prediction model for collaborative filtering
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A new user similarity measure in a new prediction model for collaborative filtering S. Manochandar 1 & M. Punniyamoorthy 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The Recommender Systems (RSs) based on the performance of Collaborative filtering (CF) depends on similarities among users or items obtained by a user-item rating matrix. The conventional measures such as the Pearson correlation coefficient (PCC), cosine (COS), and Jaccard (JACC) provide a varied and dissimilar value when the ratings between the users lie in the positive and negative side of the rating scale. These measures are also not very effective when there is sparsity in the rating matrix of the user-item. These problems are addressed by the Proximity-Impact-Popularity (PIP) similarity measure. Even though the PIP method provides an improved solution for this problem, the range of values for each component in PIP is very high. To address this issue and to improve the performance of a CF-based RS, a modified proximity-impact-popularity (MPIP) similarity measure is introduced. The expression is designed to get PIP values within the range of 0 to 1. A modified prediction expression is proposed to predict the available and unavailable ratings by combining user- and item-related components. The proposed method is tested by using various benchmark datasets. The size of the user-item sparse matrix varies to compare the performance of the methods in terms of mean absolute error, root mean squared error, precision, recall, and F1-measure. The performance of the proposed method is statistically tested through the Friedman and McNemer test. The results obtained by using the evaluation criteria indicate that the proposed method provides a better solution than the conventional methods. The statistical analysis reveals that the proposed method provides minimum MAE and RMSE values. Similarly, it also provides a maximum F1-measure for all the sub-problems. Keywords Collaborative filtering . Prediction . Proximity-impact-popularity . Similarity measure . Recommender systems
1 Introduction A majority of people spend an increasing amount of time on the Internet because of the excessive quantity of information on various fields it provides [1–5]. A Recommender System (RS) refers to the collection of information on websites related to the user preferences for a set of items. The RS employs different online information sources to predict the users’ preferences for these items [6, 7]; therefore, it plays a vital role in the sale of products or services [8, 9]. Many users prefer to buy the products based on the recommendations of other users; thus, the preferences of the users for different products should be analyzed. From the perspective of a company, this helps to maximize profits and promote its products or services.
* M. Punniyamoorthy [email protected]; [email protected] 1
National Institute of Technology, Tiruchirappalli, Tamilnadu 620 015, India
The RS methods based on the Collaborative filtering (CF) are incredibly popu
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