Collaborative Filtering Based Recommender Systems Research Articles

Time-Aware Similarity Integration for User-Based Collaborative Filtering

A time-aware collaborative filtering-based recommender system provides item recommendations for the current user by prioritizing recent items preferred by similar neighbors over past preferred items. The similarity measure critically affects the performance of the system, and this study focuses on measuring the similarity between users that changes over time. After dividing the users’ rating time into intervals and computing similarity for each time interval, the final similarity is generated as a weighted sum by assigning lower weights to past similarity values and higher weights to more recent similarity values. Additionally, to ensure continuity of similarity measurement, consecutive time intervals are set to overlap. As a result of experiments applying the proposed method to the existing similarity measures, significant performance improvement was achieved in terms of some of the major performance metrics. In particular, the degree of coverage improvement was the highest, and the performance improvement effect was higher when the overlap size between time intervals was large rather than when it was small.

A Collaborative Filtering-Based Recommender Systems approach for Multifarious applications

Recommender systems are crucial in today's IT landscape, enhancing user experiences in various industries. Collaborative filtering (CF) is a key approach, using historical interaction data to predict user preferences. This paper presents CF advancements, with a focus on latent factor models that represent users and items in a compact feature space. It also addresses sparsity issues with techniques like neighborhood-based approaches and content augmentation. Contextual CF, which incorporates temporal and contextual dynamics, is explored through methods like matrix factorization with side information and session-based recommendation. Evaluation metrics such as MAE and RMSE, along with novel ranking-based metrics, provide a comprehensive assessment of recommendation quality. In this paper we outline cutting-edge CF techniques, showcasing their mechanisms and applications and were able to achieve accurate recommendations of almost 90% using MAE and RMSE metrics. By integrating latent factor modeling, sparsity mitigation, contextual enrichment, and advanced evaluation, it paves the way for the next generation of personalized recommendation systems, tailored to meet evolving demands in modern information environments.

Methods of preference aggregation in group recommender systems

The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.

Handling Massive Sparse Data in Recommendation Systems

Collaborative filtering-based recommendation systems have become significant in various domains due to their ability to provide personalised recommendations. In e-commerce, these systems analyse the browsing history and purchase patterns of users to recommend items. In the entertainment industry, collaborative filtering helps platforms like Netflix and Spotify recommend movies, shows and songs based on users’ past preferences and ratings. This technology also finds significance in online education, where it assists in suggesting relevant courses and learning materials based on a user’s interests and previous learning behaviour. Even though much research has been done in this domain, the problems of sparsity and scalability in collaborative filtering still exist. Data sparsity refers to too few preferences of users on items, and hence it would be difficult to understand users’ preferences. Recommendation systems must keep users engaged with fast responses, and hence there is a challenge in handling large data as these days it is growing quickly. Sparsity affects the recommendation accuracy, while scalability influences the complexity of processing the recommendations. The motivation behind the paper is to design an efficient algorithm to address the sparsity and scalability problems, which in turn provide a better user experience and increased user satisfaction. This paper proposes two separate, novel approaches that deal with both problems. In the first approach, an improved autoencoder is used to address sparsity, and later, its outcome is processed in a parallel and distributed manner using a MapReduce-based [Formula: see text]-means clustering algorithm with the Elbow method. Since the [Formula: see text]-means clustering technique uses a predetermined number of clusters, it may not improve accuracy. So, the elbow method identifies the optimal number of clusters for the [Formula: see text]-means algorithm. In the second approach, a MapReduce-based Gaussian Mixture Model (GMM) with Expectation-Maximization (EM) is proposed to handle large volumes of sparse data. Both the proposed algorithms are implemented using MovieLens 20M and Netflix movie recommendation datasets to generate movie recommendations and are compared with the other state-of-the-art approaches. For comparison, metrics like RMSE, MAE, precision, recall, and F-score are used. The outcomes demonstrate that the second proposed strategy outperformed state-of-the-art approaches.

Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems

Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems

Enhancing the accuracy of collaborative filtering based recommender system with novel similarity measure

Enhancing the accuracy of collaborative filtering based recommender system with novel similarity measure

The use of restricted Boltzmann machines for clustering collaborative filtering

<div><p class="Abstract">Collaborative filtering-based recommender systems have been successfully serviced through commercial online systems to assist people with searching the information useful to them. However, several problems inherent in such systems still exist, although a lot of research work has been devoted to finding solutions. This work focuses on clustering collaborative filtering to address the scalability problem. It proposes a novel method to determine the clustering criteria for enhancing the prediction and recommendation accuracy of the systems, which is typically degraded when the clustering algorithm is integrated into collaborative filtering. We use a restricted Boltzmann machine to find the genre preference of users, which is then inputted into the clustering algorithm to cluster users. Various experiments are conducted to evaluate the performance of the proposed method. As a result, our method showed superior performance in terms of various performance criteria compared to previous clustering collaborative filtering methods and some of the major traditional systems.</p></div>

A Hybrid Deep Ranking Weighted Multi-Hashing Recommender System

In countries where there is a low availability of resources for language, businesses face the challenge of overcoming language barriers to reach their customers. One possible solution is to use collaborative filtering-based recommendation systems in their native languages. These systems employ algorithms that understand the customers’ preferences and suggest products or services in their native language. Collaborative filtering (CF) is a popular recommendation technique that simulates word-of-mouth phenomena. However, the accuracy of a CF recommendation can be affected by sparse data. In this research paper, we present a novel hybrid weighted multi-deep ranking supervised hashing (HWMDRH) approach. Our method leverages both user-based and item-based CF by merging the item-based deep ranking weighted multi-hash recommender system prediction with the user-based deep ranking weighted multi-hash recommender system prediction to generate Top-N prediction. We conducted extensive experiments on the MovieLens 1M dataset, and our results show that the proposed HWMDRH model outperforms existing models and achieves state-of-the-art performance across recall, precision, RMSE, and F1-score metrics.

The use of restricted Boltzmann machines for clustering collaborative filtering

<div><p class="Abstract">Collaborative filtering-based recommender systems have been successfully serviced through commercial online systems to assist people with searching the information useful to them. However, several problems inherent in such systems still exist, although a lot of research work has been devoted to finding solutions. This work focuses on clustering collaborative filtering to address the scalability problem. It proposes a novel method to determine the clustering criteria for enhancing the prediction and recommendation accuracy of the systems, which is typically degraded when the clustering algorithm is integrated into collaborative filtering. We use a restricted Boltzmann machine to find the genre preference of users, which is then inputted into the clustering algorithm to cluster users. Various experiments are conducted to evaluate the performance of the proposed method. As a result, our method showed superior performance in terms of various performance criteria compared to previous clustering collaborative filtering methods and some of the major traditional systems.</p></div>

Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations.

A novel model based collaborative filtering recommender system via truncated ULV decomposition

Collaborative filtering is a technique that takes into account the common characteristics of users and items in recommender systems. Matrix decompositions are one of the most used techniques in collaborative filtering based recommendation systems. Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) based approaches are widely used. Although they are quite good at dealing with the scalability problem, their complexities are high. In this study, the Truncated-ULV decomposition (T-ULVD) technique was used as an alternative technique to improve the accuracy and quality of recommendations. The proposed method has been tested with Movielens 100 k, Movielens 1 M, Filmtrust, and Netflix datasets, which are widely used in recommender system researches. In order to assess the performance of the proposed model, standart metrics (MAE, RMSE, precision, recall, and F1 score) were used. It is seen that while progress was achieved in all experiments with the T-ULVD compared to the NMF, very close or better results were obtained compared to the SVD. Moreover, this study may guide T-ULVD based future studies on solving the cold-start problem and reducing the sparsity in collaborative filtering based recommender systems.

Data-Driven Self-Organization With Implicit Self-Coordination for Coverage and Capacity Optimization in Cellular Networks

Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) are two tightly coupled and conflicting Self-Organizing Network (SON) functions that are responsible for ensuring optimal coverage and capacity in any cellular network. While executing currently, these functions may modify the same RF and antenna parameters, resulting in severe performance deteriorations. In this context, a centralized optimization and coordination approach may be impractical considering the large sizes of network clusters and the dynamics involved between the several other defined SON use cases. In this work, an implicitly coordinated and scalable self-organizing architecture is followed such that when a carefully defined multi-objective utility function for CCO-ICIC joint optimization is optimized locally by each RAN node, a desired balance between the two conflicting network targets of coverage and capacity is ensured globally. Pareto analysis of three variants of the proposed Local Multi-Objective KPI (LMO KPI) has been conducted to implicitly coordinate the two SON functions in a distributed self-organized manner. In order to recommend appropriate network configurations dynamically to quickly adapt to altering network environments, two collaborative filtering-based Recommender Systems (RecSys), one using a Deep Autoencoder and another based on Singular Value Decomposition, have been employed along with a neural network regressor to improve recommendations for cold-start scenarios. The two proposed hybrid-RecSys-based SON coordination solutions, while adopting an appropriate Local Multi-Objective KPI (LMO KPI), outperform previous work in coverage by 36% and in capacity by around 2% while reducing power consumption by more than 50%. The study demonstrates that the definition of the LMO KPI is crucial to the performance of this approach. Altogether, the work shows that the adopted self-organization and implicit SON-coordination approach is not only feasible and performant but also scales well if implemented meticulously.

Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique

The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.

Deep variational models for collaborative filtering-based recommender systems

Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems. Our proposed models apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing the variational technique in the neural collaborative filtering field. This method does not depend on the particular model used to generate the latent representation. In this way, this approach can be applied as a plugin to any current and future specific models. The proposed models have been tested using four representative open datasets, three different quality measures, and state-of-the-art baselines. The results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect. Additionally, a framework is provided to enable the reproducibility of the conducted experiments.

A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System

Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user’s ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers–Jaccard–Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.

Personalized Relationships-Based Knowledge Graph for Recommender Systems with Dual-View Items

The knowledge graph has received a lot of interest in the field of recommender systems as side information because it can address the sparsity and cold start issues associated with collaborative filtering-based recommender systems. However, when incorporating entities from a knowledge graph to represent semantic information, most current KG-based recommendation methods are unaware of the relationships between these users and items. As such, the learned semantic information representation of users and items cannot fully reflect the connectivity between users and items. In this paper, we present the PRKG-DI symmetry model, a Personalized Relationships-based Knowledge Graph for recommender systems with Dual-view Items that explores user-item relatedness by mining associated entities in the KG from user-oriented entity view and item-oriented entity view to augment item semantic information. Specifically, PRKG-DI utilizes a heterogeneous propagation strategy to gather information on higher-order user-item interactions and an attention mechanism to generate the weighted representation of entities. Moreover, PRKG-DI provides a score feature as a filter for individualized relationships to evaluate users’ potential interests. The empirical results demonstrate that our approach significantly outperforms several state-of-the-art baselines by 1.6%, 2.1%, and 0.8% on AUC, and 1.8%, 2.3%, and 0.8% on F1 when applied to three real-world scenarios for music, movie, and book recommendations, respectively.

Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset.

An improved similarity measure for collaborative filtering-based recommendation system

Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users’ tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments.

Hybrid content and collaborative filtering based recommendation system for e-learning platforms

Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of e-learning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to generalize across content sources and content types. In this work, we present a hybrid recommendation system that combines the desirable characteristics of collaborative filtering, as well as from content-based filtering, for the task of recommending course content/curriculum to users of an e-learning system. Our recommendation easily incorporates changing user profiles (as learners step through course content) and also generalize across content sources (courses taught by various departments) and types. We apply our system on a real dataset comprising 111 students organized into interdisciplinary groups. Our results showcase the clear benefits that our hybrid recommendation system enjoys, showing more than 30 percentage points of improvement over conventional filtering techniques.

Integrating User-Group relationships under interest similarity constraints for social recommendation

Traditional collaborative filtering based recommender systems generally suffer from the interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate the issue and improve recommendation performance by introducing social information. Existing social recommendation studies primarily focus on the direct connections between users, such as friendship or users’ correlation. Unfortunately, there often is a severe data sparsity issue in above social data as well, which limits the performance of these models. In contrast, user-group relationships, another valuable social information that is formed by users joining the groups they are interested in, have received insufficient attention. In this paper, we focus on this relationship, demonstrate its excellent effectiveness in alleviating the problem of data sparsity, and integrate it into our recommendation model IGRec (Integrating user-Group relationships for social Recommendation) in a reasonable way. Specifically, to address the problem that existing group-information-enhanced methods have not modeled users’ collaborative interests and social influence in depth, we reformulate the available data into two bipartite graphs: user-item graph and user-group graph. And then employ more robust high-order GCN-based model combining a multi-layer attention mechanism to learn user and item representation from two graphs. Furthermore, we notice that due to the high complexity of user-group networks, the interests of some users in the same group may be far different, especially in those large-scale groups. The indiscriminate use of high-order neighbors’ information in user-group graph may result in the introduction of negative information during the embedding propagation. Thus, to obtain a more precise representation for user and item, we propose to constrain the graph convolution operations at the social side inside subgraphs composed of users with similar interests and the groups they have joined in our model. Finally, experimental results on three real-world datasets clearly show the effectiveness of our proposed model.