COLLABORATIVE FILTERING FOR MULTI-CLASS DATA USING BAYESIAN NETWORKS

2008 ◽  
Vol 17 (01) ◽  
pp. 71-85 ◽  
Author(s):  
XIAOYUAN SU ◽  
TAGHI M. KHOSHGOFTAAR
Keyword(s):  
Logistic Regression ◽  
Bayesian Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Real World ◽  
Incomplete Data ◽  
Pearson Correlation ◽  
Bayesian Classifiers ◽  
Rating Data ◽  
Better Than

As one of the most successful recommender systems, collaborative filtering (CF) algorithms are required to deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian networks (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works on applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers.1,2 In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, the extended logistic regression on tree augmented naïve Bayes (TAN-ELR)3 CF model consistently performs better than the traditional Pearson correlation-based CF algorithm for the rating data that have few items or high missing rates. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases.

scholarly journals Further Improvement on Two-Way Cooperative Collaborative Filtering Approaches for the Binary Market Basket Data

2021 ◽  
Vol 11 (19) ◽  
pp. 8977
Author(s):  
Wook-Yeon Hwang ◽  
Jong-Seok Lee
Keyword(s):  
Logistic Regression ◽  
Collaborative Filtering ◽  
Random Forests ◽  
Pearson Correlation ◽  
Main Idea ◽  
Experimental Results ◽  
Market Basket ◽  
Regression Approach ◽  
Cold Start Problem ◽  
Better Than

Two-way cooperative collaborative filtering (CF) has been known to be crucial for binary market basket data. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, a random forests (RF) R-square-based score, an RF Pearson correlation-based score, and a CF scheme based on the RF R-square-based score. The main idea is to utilize as much predictive information as possible within the two-way prediction in order to cope with the cold-start problem. All of the proposed methods work better than the existing two-way cooperative CF approach in terms of the experimental results.

scholarly journals Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

Rekayasa ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 234-239
Author(s):  
Noor Ifada ◽  
Nur Fitriani Dwi Putri ◽  
Mochammad Kautsar Sophan
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Method Development ◽  
Recommendation System ◽  
Rating Data ◽  
The Real ◽  
Rating Prediction ◽  
Filtering Approach ◽  
Better Than

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.

Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach

2014 ◽  
Vol 1 (4) ◽  
pp. 34-50
Author(s):  
Roee Anuar ◽  
Yossi Bukchin ◽  
Oded Maimon ◽  
Lior Rokach
Keyword(s):  
Recommender Systems ◽  
Entropy Measure ◽  
Continuous Data ◽  
System Evaluation ◽  
Rating Data ◽  
New Approach ◽  
Selection Of ◽  
Classical Entropy ◽  
Better Than

The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative filtering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selection. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifies the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it finally develops the consent entropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offline experiments using the Netflix database. The experimental results confirm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639.

Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

2016 ◽  
Vol 8 (2) ◽  
pp. 16-26 ◽  
Author(s):  
Zhihai Yang ◽  
Zhongmin Cai
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Graph Mining ◽  
Scale Up ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Rating Systems ◽  
Rating Data ◽  
Online Rating ◽  
Two Stages

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2510
Author(s):  
Aaron Ling Chi Yi ◽  
Dae-Ki Kang
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Real World ◽  
Experimental Analysis ◽  
Location Awareness ◽  
Research Communities ◽  
Current Location ◽  
Real World Datasets ◽  
Filtering Approach

Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.

scholarly journals A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

2021 ◽  
Vol 10 (3) ◽  
pp. 727
Author(s):  
Tajul Rosli Razak ◽  
Mohammad Hafiz Ismail ◽  
Shukor Sanim Mohd Fauzi ◽  
Ray Adderley JM Gining ◽  
Ruhaila Maskat
Keyword(s):  
Artificial Intelligence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Real World ◽  
Participatory Design ◽  
Design Methodology ◽  
Multiple Correlation

<span lang="EN-GB">A recommender system is an algorithm aiming at giving suggestions to users on relevant elements or items such as products to purchase, books to read, jobs to apply or anything else depending on industries or situations. Recently, there has been a surge in interest in developing a recommender system in a variety of areas. One of the most widely used approaches in recommender systems is collaborative filtering (CF). The CF is a strategy for automatically creating a filter based on a user's needs by extracting desires or recommendation information from a large number of users. The CF approach uses multiple correlation steps to do this. However, the occurrence of uncertainty in finding the best similarity measure is unavoidable. This paper outlines a method for improving the configuration of a recommender system that is tasked with recommending an appropriate study field and supervisor to a group of final-year project students. The framework we suggest is built on a participatory design methodology that allows students' individual opinions to be factored into the recommender system's design. The architecture of the recommender scheme was also illustrated using a real-world scenario, namely mapping the students' field of interest to a possible supervisor for the final year project.</span>

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.

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.

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