A new user similarity measure in a new prediction model for collaborative filtering

2020 ◽  
Vol 51 (1) ◽  
pp. 586-615
Author(s):  
S. Manochandar ◽  
M. Punniyamoorthy
Keyword(s):  
Prediction Model ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
User Similarity

scholarly journals UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 122-130
Author(s):  
Yue Huang ◽  
Xuedong Gao ◽  
Shujuan Gu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Accurate Prediction ◽  
Similarity Measurement ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Level Difference

Abstract User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.

A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data

2018 ◽  
Vol 45 (5) ◽  
pp. 656-675 ◽  
Author(s):  
Jiangzhou Deng ◽  
Yong Wang ◽  
Junpeng Guo ◽  
Yongheng Deng ◽  
Jerry Gao ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Predictive Performance ◽  
Probability Density Distribution ◽  
User Similarity ◽  
Kl Divergence ◽  
Active User ◽  
Leibler Divergence ◽  
Sparsity Problem

In the neighbourhood-based collaborative filtering (CF) algorithms, a user similarity measure is used to find other users similar to an active user. Most of the existing user similarity measures rely on the co-rated items. However, there are not enough co-rated items in sparse dataset, which usually leads to poor prediction. In this article, a new similarity scheme is proposed, which breaks free of the constraint of the co-rated items. Moreover, an item similarity measure based on the Kullback–Leibler (KL) divergence is presented, which identifies the relation between items based on the probability density distribution of ratings. Since the item similarity based on KL divergence makes full use of all ratings, it owns better flexibility for sparse datasets. The CF algorithm using our proposed similarity scheme is implemented and compared with some classic CF algorithms. The compared results show that the CF using our similarity has better predictive performance. Therefore, our similarity scheme is a good solution for the sparsity problem and has great potential to be applied to recommendation systems.

ASSIST IN COOKING: ACTION SUPPORT SYSTEM FOR INTERACTIVE SELF-TRAINING

2008 ◽  
Vol 02 (02) ◽  
pp. 207-233
Author(s):  
SATORU MEGA ◽  
YOUNES FADIL ◽  
ARATA HORIE ◽  
KUNIAKI UEHARA
Keyword(s):  
Human Computer Interaction ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Support System ◽  
Mixed Reality ◽  
Flexible Learning ◽  
Virtual Objects ◽  
Cooking Process ◽  
Real Objects ◽  
Transition Models

Human-computer interaction systems have been developed in recent years. These systems use multimedia techniques to create Mixed-Reality environments where users can train themselves. Although most of these systems rely strongly on interactivity with the users, taking into account users' states, they still lack the possibility of considering users preferences when they help them. In this paper, we introduce an Action Support System for Interactive Self-Training (ASSIST) in cooking. ASSIST focuses on recognizing users' cooking actions as well as real objects related to these actions to be able to provide them with accurate and useful assistance. Before the recognition and instruction processes, it takes users' cooking preferences and suggests one or more recipes that are likely to satisfy their preferences by collaborative filtering. When the cooking process starts, ASSIST recognizes users' hands movement using a similarity measure algorithm called AMSS. When the recognized cooking action is correct, ASSIST instructs the user on the next cooking procedure through virtual objects. When a cooking action is incorrect, the cause of its failure is analyzed and ASSIST provides the user with support information according to the cause to improve the user's incorrect cooking action. Furthermore, we construct parallel transition models from cooking recipes for more flexible instructions. This enables users to perform necessary cooking actions in any order they want, allowing more flexible learning.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lili Wang ◽  
Ting Shi ◽  
Shijin Li
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recall Rate ◽  
Cosine Similarity ◽  
Fusion Method ◽  
Complex Matrix ◽  
Recommendation Algorithm ◽  
Kl Divergence ◽  
Experimental Effect ◽  
User Recommendation

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

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