Filtering Recommender Systems Research Articles

Light dual hypergraph convolution for collaborative filtering

Recommender systems filter information to meet users’ personalized interests actively. Existing graph-based models typically extract users’ interests from a heterogeneous interaction graph. They do not distinguish learning between users and items, ignoring the heterogeneous property. In addition, the interaction sparsity and long-tail bias issues still limit the recommendation performance significantly. Fortunately, hidden homogeneous correlations that have a considerable volume can entangle abundant CF signals. In this paper, we propose a light dual hypergraph convolution (LDHC) for collaborative filtering, which designs a hypergraph to involve heterogeneous and homogeneous correlations with more CF signals confronting the challenges. Over the integrated hypergraph, a two-level interest propagation is performed within the heterogeneous interaction graph and between the homogeneous user/item graphs to model users’ interests, where learning on users and items is distinguished and collaborated by the homogeneous propagation. Specifically, hypergraph convolution is lightened by removing unnecessary parameters to propagate users’ interests. Extensive experiments on publicly available datasets demonstrate that the proposed LDHC outperforms the state-of-the-art baselines.

Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems

Recommender systems aim to predict user preferences by analyzing users' past behavior. Collaborative filtering (CF) is one of the key techniques employed in recommender systems that uses explicit ( e.g. , ratings) and implicit ( e.g. , browsing) feedback from users to predict unknown feedback, providing top- N recommendations. However, CF faces challenges when dealing with sparse data, which can decrease the accuracy of recommendations. To overcome these inherent challenges in recommender systems, this article introduces the concept of "uninteresting items" that have not been rated by a user, but are unlikely to be liked even when recommended. We then review our previous works that utilize both positive preferences from rated items and negative preferences from uninteresting items to improve recommendation accuracy. Specifically, we discuss a family of our eight CF methods that are assisted by the uninteresting items: Zero-injection (ZI), l -injection, Imputation, RAGAN, and Deep-ZI, which are designed for explicit feedback, as well as gOCCF, M-BPR, and CNS, which are designed for implicit feedback. Also, we report some evaluation results for showing their effectiveness.

Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering

A network-based method applied to collaborative filtering in recommender systems is introduced in this paper. Specifically, a novel mixed-membership stochastic block model with a conjugate prior from the exponential family is proposed for bipartite networks. The analytical expression of the model is derived, and a variational Bayesian algorithm that is computationally feasible for approximating the untractable posterior distributions is presented. Extensive simulations show that the proposed model provides more accurate inference than competing methods with the presence of outliers. The proposed model is also applied to a MovieLens dataset for a real data application.

CoBERT: A Contextual BERT model for recommending employability profiles of information technology students in unstable developing countries

Unemployment constitutes one of the major problems in developing countries, with factors such as unavailable skills and the proliferation of unskilled workers being cited as main causes. The existing literature has shown that using social networks analysis techniques, employment options for students and graduates can be accurately generated through recommender systems (RS) to optimize the chances of employment. Therefore, this study proposes a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model named Contextual BERT (CoBERT) for item-centered content-based (CB) filtering RS. The model uses socio-political background, graduate-employer relationship and graduate academic competencies of information technology (IT) students as contextual factors to recommend employability profiles of IT students in unstable developing countries (UDCs) such as the Democratic Republic of Congo (DRC). The performance of the proposed model is verified using four metrics. To evaluate the online performance of the model, the metrics of Diversity and Novelty are used. The metrics of normalized discounted cumulative gain (nDCG) and mean average precision (MAP) are applied to evaluate the model offline. The proposed model shows a high nDCG value at Top-N=4, equaling to 0.99. Validation statistics of 0.5 MAP score and significant t-statistics test values for Diversity and Novelty indicate that the proposed model can be generalized in the DRC and other similar UDCs. The RS model proposed in the current study contributes to the literature on employability models by advancing beyond traditional approaches such as fuzzy logic (FL), and also by taking graduate skills into account. The proposed model is a novel employability recommendation technique that provides highly precise and contextual recommendations. The output from this study contributes a theoretical foundation for implementing RSs in education and the industry.

Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions

Preventive care helps patients identify and address medical issues early when they are easy to treat. The internet offers vast information about preventive measures, but the sheer volume of data can be overwhelming for individuals to process. To help individuals navigate this information, recommender systems filter and recommend relevant information to specific users. Despite their popularity in other fields, such as e-commerce, recommender systems have yet to be extensively studied as tools to support the implementation of prevention strategies in health care. This underexplored area presents an opportunity for recommender systems to serve as a complementary tool for medical professionals to enhance patient-centered decision-making and for patients to access health information. Thus, these systems can potentially improve the delivery of preventive care. This study proposes practical, evidence-based propositions. It aims to identify the key factors influencing patients' use of recommender systems and outlines a study design, methods for creating a survey, and techniques for conducting an analysis. This study proposes a 6-stage approach to examine user perceptions of the factors that may influence the use of recommender systems for preventive care. First, we formulate 6 research propositions that can be developed later into hypotheses for empirical testing. Second, we will create a survey instrument by collecting items from extant literature and then verify their relevance using expert analysis. This stage will continue with content and face validity testing to ensure the robustness of the selected items. Using Qualtrics (Qualtrics), the survey can be customized and prepared for deployment on Amazon Mechanical Turk. Third, we will obtain institutional review board approval because this is a human subject study. In the fourth stage, we propose using the survey to collect data from approximately 600 participants on Amazon Mechanical Turk and then using R to analyze the research model. This platform will serve as a recruitment tool and the method of obtaining informed consent. In our fifth stage, we will perform principal component analysis, Harman Single Factor test, exploratory factor analysis, and correlational analysis; examine the reliability and convergent validity of individual items; test if multicollinearity exists; and complete a confirmatory factor analysis. Data collection and analysis will begin after institutional review board approval is obtained. In pursuit of better health outcomes, low costs, and improved patient and provider experiences, the integration of recommender systems with health care services can extend the reach and scale of preventive care. Examining recommender systems for preventive care can be vital in achieving the quadruple aims by advancing the steps toward precision medicine and applying best practices. PRR1-10.2196/43316.

Resource Management and Optimization Method of Music Audio-Visual Archives under the Background of Big Data

With the development of music and video, music and video management has been in a relatively backward state. This study uses the logistic regression algorithm based on sigmoid functions to analyze and process the music audio-visual library of a music software and establishes the logistic regression dynamic model, which provides a scientific research method for this complex system. The LPC characteristic coefficient is extracted, and then the logistic regression model of music audio-visual archives resources is established. After completing the model, this study obtains the logistic regression model through a series of experiments, which effectively optimizes the management of music audio-visual archives. The specific experimental conclusions are as follows: through genre classification and emotion classification, users can search more music audio-visual archives resources. The comparison shows that the recommendation effect after filtering by the logistic regression model is better than that of the nonstandard collaborative filtering recommendation system. Finally, after practical applications, it is concluded that the model based on the logistic regression algorithm has made a good optimization conclusion for the resource management of music audio-visual archives.

Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce

One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07

Design of Garment Style Recommendation System Based on Interactive Genetic Algorithm.

Recommender systems provide users with product information and suggestions, which has gradually become an important research tool in e-commerce IT technology, which has attracted a lot of attention of researchers. Collaborative filtering recommendation technology has been the most successful recommendation technology so far, but there are two major problems—recommendation quality and scalability. At present, research at home and abroad mainly focuses on recommendation quality, and there is less discussion on scalability. The scalability problem is that as the size of the system increases, the response time of the system increases to a point where users cannot afford it. Existing solutions often result in a significant drop in recommendation quality while reducing recommendation response time. In this paper, the clustering analysis subsystem based on the genetic algorithm is innovatively introduced into the traditional collaborative filtering recommendation system, and its design and implementation are given. In addition, when obtaining the nearest neighbors, only the clustered users of the target user are searched, making it a collaborative filtering recommender system based on genetic clustering. The experimental results show that the response time of the traditional collaborative filtering recommender system increases linearly with the increase in the number of users while the response time of the collaborative filtering recommender system based on genetic clustering remains unchanged with the increase in the number of users. On the other hand, the recommendation quality of the collaborative filtering recommender system based on genetic clustering is basically not degraded compared with that of the traditional collaborative filtering recommender system. Therefore, the collaborative filtering recommender system based on genetic clustering can effectively solve the scalability problem of the collaborative filtering recommender system.

Feature-blind fairness in collaborative filtering recommender systems

Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions. These recommenders started being adopted in several domains, and were identified as generating biased results that could harm the data items being recommended. The exposure in generated rankings, for instance in a job candidate selection situation, is supposed to be fairly distributed among candidates, regardless of their sensitive attributes (gender, race, nationality, age) for promoting equal opportunities. It can happen, however, that no such sensitive information is available in the data applied for training the recommender, and in this case, there is still space for biases that can lead to unfair treatment, named Feature-Blind unfairness. In this work, we adopt Variational Autoencoders (VAE), considered as the state-of-the-art technique for Collaborative Filtering (CF) recommendations, and we present a framework for addressing fairness when having only access to information about user-item interactions. More specifically, we are interested in Position and Popularity Bias. VAE loss function combines two terms associated with accuracy and quality of representation; we introduce a new term for encouraging fairness, and demonstrate the effect of promoting fair results despite of a tolerable decrease in recommendation quality. In our best scenario, position bias is reduced by 42% despite a reduction of 26% in recall in the top 100 recommendation results, compared to the same situation without any fairness constraints.

SVD- Initialized K-Means Clustering for Collaborative Filtering Recommender Systems

SVD- Initialized K-Means Clustering for Collaborative Filtering Recommender Systems

SVD-initialised K-means clustering for collaborative filtering recommender systems

SVD-initialised K-means clustering for collaborative filtering recommender systems

Shilling attack detection for collaborative recommender systems: a gradient boosting method.

Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods only use a single classifier to handle the classification of malicious attackers. Considering the above disadvantages, this paper proposes a gradient boosting method (named XGB-SAD) to achieve attack detection by combining double-view and gradient boosting. We first analyze the rating matrix with a double-view of time and item, which in turn defines the TPUS collection. Then our method uses eXtreme Gradient Boosting to perform heuristic iterative optimization of the model's objective function and uses the idea of ensemble learning to integrate multiple sets of base classifiers into strong classifier. The integrated strong classifiers are used to complete the detection of malicious attackers. Finally, we perform several experiments and the results demonstrate that XGB-SAD outperforms the comparison methods in terms of small-scale attack detection and overall detection, which proves the performance of our method.

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph.

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

Contextual Personality-Aware Recommender System Versus Big Data Recommender System

Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems

Since the rise of E-commerce companies and many other web services, the applications of recommender systems have been adopted more broadly than ever before. Although collaborative filtering is the most well-known approach which utilizes customer’s preference to discover their interest, the problems of data sparsity and similarities selection still exist in it. Thus, this study intends to propose a hybrid metaheuristic with perturbation-based K-nearest neighbors and densest imputation for collaborative filtering (KDI-KNN) algorithm to reduce the effects of data sparsity. A similarities union function is proposed to determine the fittest similarity and enhance the prediction performance. Eventually, the experimental results indicate that hybrid metaheuristics with perturbation-based KDI-KNN algorithms are superior to basic KNN, original KDI-KNN, and most single metaheuristic-based KDI-KNN. In addition, a real-world dataset, fund transaction dataset is adopted in the case study. The analysis reveals that the similarity is seriously affected by the different content of the dataset.

Iterative rating prediction for neighborhood-based collaborative filtering

This paper investigates the issue of rating prediction for neighborhood-based collaborative filtering in recommendation systems. A novel rating prediction algorithm, called iterative rating prediction (IRP), is proposed for neighborhood-based collaborative filtering. The main idea behind IRP is neighborhood propagation. To predict ratings of items for target users, IRP relies on not only the rating information of direct neighbors but also that of indirect neighbors with different propagation depth. To implement the idea, IRP iteratively updates the ratings of items for users. The efficiency of the proposed method is examined through extensive experiments. Experimental results demonstrate the superior performance of our method, especially on small-scaled and sparse datasets.

Intent-Based Network Policy to Solution Architecting Recommendations

The semantic diversity of policies written by people with different IT literacy to achieve certain network security or performance goals created ambiguity to otherwise straightforward solution implementations. In this project, an intent-aware solution recommender is designed to decode semantic cues in network policies written by various demographics for robust solution recommendations. A novel policy analyzer is designed to extract the intrinsic networking intents from ICT policies to provide context-specific solution recommendations. A custom network-aware intent recognizer is trained on a small keywords-to-intents dataset annotated by domain experts using NLP algorithms in AWS comprehend. The bin-of-words model is then used to classify sentences in the policies into predicted ‘intent' class. A collaborative filtering recommendation system using crowd-sourced ground-truth is designed to suggest optimal architecting solutions to achieve the requirements outlined in ICT policies.

Leveraging User Comments for Recommendation in E-Commerce

Collaborative filtering recommender systems traditionally recommend products to users solely based on the given user-item rating matrix. Two main issues, data sparsity and scalability, have long been concerns. In our previous work, an approach was proposed to address the scalability issue by clustering the products using the content of the user-item rating matrix. However, it still suffers from these concerns. In this paper, we improve the approach by employing user comments to address the issues of data sparsity and scalability. Word2Vec is applied to produce item vectors, one item vector for each product, from the comments made by users on their previously bought goods. Through the user-item rating matrix, the user vectors of all the customers are produced. By clustering, products and users are partitioned into item groups and user groups, respectively. Based on these groups, recommendations to a user can be made. Experimental results show that both the inaccuracy caused by a sparse user-item rating matrix and the inefficiency due to an enormous amount of data can be much alleviated.

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

An empirical study of alternating least squares collaborative filtering recommendation for Movielens on Apache Hadoop and Spark

In recent years, both consumers and businesses have faced the problem of information explosion, and the recommendation system provides a possible solution. This study implements a movie recommendation system that provides recommendations to consumers in an effort to increase consumer spending while reducing the time between film selection. This study is a prototype of collaborative filtering recommendation system based on Alternating Least Squares (ALS) algorithm. The advantage of collaborative filtering is that it avoids possible violations of the Personal Data Protection Act and reduces the possibility of errors due to poor quality of personal data. Our research improves the ALS's limited scalability by using a platform that combines Spark with Hadoop Yarn and uses this combination to calculate movie recommendations and store data separately. Based on the results of this study, our proposed system architecture provides recommendations with satisfactory accuracy while maintaining acceptable computational time with limited resources.