scholarly journals Cognitive Similarity-Based Collaborative Filtering Recommendation System

2020 ◽  
Vol 10 (12) ◽  
pp. 4183 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Min-Sung Hong ◽  
Jason J. Jung ◽  
Bong-Soo Sohn
Keyword(s):  
Performance Evaluation ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Cognitive Network ◽  
Data Set ◽  
New Approach ◽  
Layered Architecture ◽  
F Measure

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

scholarly journals Job Recommendation System using Content and Collaborative Filtering based Techniques

2019 ◽  
Vol 9 (3) ◽  
pp. 8-15 ◽  
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Information Overload ◽  
Pearson Correlation ◽  
Job Applicants ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Proposed Model ◽  
Job Recommendation ◽  
Fitting In

Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recommendations based on candidates’ skills identified from past jobs in which they have applied and CF model makes recommendations based on jobs in which similar users have applied and also those jobs in which that user has applied frequently together in very similar contexts using Word2Vec’s skip-gram model. We used k-Nearest Neighbors technique and Pearson Correlation Coefficient. The recall of our proposed model is found to be 63.97% on a data set which had nearly 1900+ jobs and 23,000 job applicants

scholarly journals Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

2020 ◽  
Vol 9 (1) ◽  
pp. 1548-1553
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
Correlation Matrix ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Vital Role ◽  
Music Recommendation ◽  
Filtering Method ◽  
Music Recommendation System

Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF

scholarly journals OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

2020 ◽  
Vol 9 (12) ◽  
pp. 711
Author(s):  
Luong Vuong Nguyen ◽  
Jason J. Jung ◽  
Myunggwon Hwang
Keyword(s):  
Recommendation System ◽  
Pearson Correlation ◽  
Cultural Tourism ◽  
User Feedback ◽  
Cross Cultural ◽  
Experimental Results ◽  
Shopping Malls ◽  
Layered Architecture ◽  
Different Cultures

This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively.

Collaborative Filtering-Based Recommendation System Using Time Decay Model

2021 ◽  
Vol 17 (3) ◽  
pp. 85-100
Author(s):  
Jayaraman Parthasarathy ◽  
Ramesh Babu Kalivaradhan
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
User Engagement ◽  
Confidence Bound ◽  
Time Decay ◽  
Specific Data ◽  
Movie Recommendation ◽  
Upper Confidence Bound ◽  
F Measure

Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.

A Novel Similarity Measure Based on Weighted Bipartite Network for Collaborative Filtering Recommendation

2012 ◽  
Vol 263-266 ◽  
pp. 1834-1837 ◽  
Author(s):  
Jian Xun Xia ◽  
Fei Wu ◽  
Chang Sheng Xie
Keyword(s):  
Collaborative Filtering ◽  
Pearson Correlation ◽  
Bipartite Network ◽  
Similarity Matrix ◽  
Data Set ◽  
User Similarity ◽  
Novel Approach ◽  
Weighted Matrix ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy

This paper presents a novel approach to compute user similarity based on weighted bipartite network and resource allocation principle for collaborative filtering recommendation. The key is to calculate the asymmetric user weighted matrix and translate it into a symmetric user similarity matrix. We carry out extensive experiments over Movielens data set and demonstrate that the proposed approach can yield better recommendation accuracy and can partly to alleviate the trouble of sparseness. Compare with traditional collaborative filtering recommendation algorithms based on Pearson correlation similarity and adjusted cosine similarity, the proposed method can improve the average predication accuracy by 6.7% and 0.6% respectively.

scholarly journals Attention neural collaboration filtering based on GRU for recommender systems

2021 ◽  
Author(s):  
Hongbin Xia ◽  
Yang Luo ◽  
Yuan Liu
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Auxiliary Information ◽  
User Preferences ◽  
Feature Representation ◽  
Long Distance ◽  
Data Set ◽  
Filtering Method ◽  
The Impact

AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

scholarly journals A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

2019 ◽  
Vol 16 (1(Suppl.)) ◽  
pp. 0263
Author(s):  
AL-Bakri Et al.
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Similarity Measures ◽  
Huge Amount ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Target User ◽  
Global Meaning ◽  
System Memory ◽  
Accuracy Of Prediction

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

RECOMMENDER SYSTEMS: PREDICTING WITH MACHINE LEARNING BASED ON POPULATION-BASED ALGORITHM

2020 ◽  
pp. 48-56
Author(s):  
S. I. Rodzin ◽  
O. N. Rodzina
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid Model ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Experimental Studies ◽  
Search Space ◽  
Population Based ◽  
Content Filtering ◽  
Population Based Algorithm

The article considers the formulation of the forecasting problem as well as such problems of recommender systems as data sparsity, cold start, scalability, synonymy, fraud, diversity, white crows. Combining the results of collaborative and content filtering gives us two possibilities. On the one hand, to weigh the results according to the content data. On the other hand, to shift these weights towards collaborative filtering as soon as data about a particular user appears. In turn, this improves the accuracy of the recommendations. The authors propose a hybrid model of a recommender system. Such a system includes the characteristics of collaborative and content filtering both. Also, the population-based algorithm for filtering and the architecture of a recommendation system based on it are described in the article. The algorithm consists of the following steps: study the search space; synthesis of solutions, i.e. points of this space; request quality assessment decisions or “fitness”; using it to make “natural selection”. Here we see the learning process about which areas of the search space contain the best solutions. The population of user “characteristics” encoded in the population-based algorithm supports a variety of input data in a hybrid model. The authors propose a coding structure for decisions in a population-based algorithm using the example of a recommender movie viewing system. Drift analysis evaluates the polynomial complexity of the algorithm. The authors demonstrate the results of experimental studies on an array of benchmarks. We also present an assessment of filtration efficiency based on a hybrid model and a population-based algorithm in comparison with the traditional method of collaborative filtering using the Pearson correlation coefficient. We can see that the prediction accuracy of the population-based algorithm is higher than that of the Pearson algorithm.

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