Approach For Recommender Systems Research Articles

Multi-modal recommender system for predicting project manager performance within a competency-based framework.

The evaluation of performance using competencies within a structured framework holds significant importance across various professional domains, particularly in roles like project manager. Typically, this assessment process, overseen by senior evaluators, involves scoring competencies based on data gathered from interviews, completed forms, and evaluation programs. However, this task is tedious and time-consuming, and requires the expertise of qualified professionals. Moreover, it is compounded by the inconsistent scoring biases introduced by different evaluators. In this paper, we propose a novel approach to automatically predict competency scores, thereby facilitating the assessment of project managers' performance. Initially, we performed data fusion to compile a comprehensive dataset from various sources and modalities, including demographic data, profile-related data, and historical competency assessments. Subsequently, NLP techniques were used to pre-process text data. Finally, recommender systems were explored to predict competency scores. We compared four different recommender system approaches: content-based filtering, demographic filtering, collaborative filtering, and hybrid filtering. Using assessment data collected from 38 project managers, encompassing scores across 67 different competencies, we evaluated the performance of each approach. Notably, the content-based approach yielded promising results, achieving a precision rate of 81.03%. Furthermore, we addressed the challenge of cold-starting, which in our context involves predicting scores for either a new project manager lacking competency data or a newly introduced competency without historical records. Our analysis revealed that demographic filtering achieved an average precision of 54.05% when dealing with new project managers. In contrast, content-based filtering exhibited remarkable performance, achieving a precision of 85.79% in predicting scores for new competencies. These findings underscore the potential of recommender systems in competency assessment, thereby facilitating more effective performance evaluation process.

MD-GCCF: Multi-view deep graph contrastive learning for collaborative filtering

Collaborative Filtering (CF), a classical recommender system approach, learns users’ interests and behavioral preferences for items through a user–item interaction graph. CF based on graph neural network (GNN) and CF based on graph contrastive learning (GCL) show strong advantages in both modeling multi-layer signals and solving label sparsity, respectively. However, there are still two key problems to be solved: Most CF models based on (1) GNN suffer from the over-smoothing problem and are unable to aggregate deep collaborative signals and (2) GCL adopts a single aggregation paradigm, resulting in a lack of diversity in the feature representation of collaborative signals. To solve the above problems, a multi-view deep graph contrastive learning for collaborative filtering (MD-GCCF) has been proposed from two perspectives. First, a deep graph collaborative signal aggregation module is proposed to learn potential intention similarity representations for deep collaborative signal propagation within a few layers. Second, a novel multi-view contrastive learning module has been proposed, utilizing both local and global contrastive learning views from the collaborative signal aggregation module to enhance deep structures and semantic features in collaborative signals. MD-GCCF improves by 9.52%, 3.34%, and 2.49% compared to the rival models, respectively, in the Amazon book, Yelp2018, and Gowalla datasets.

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.

A graph neural approach for group recommendation system based on pairwise preferences

Pairwise preference information, which involves users expressing their preferences by comparing items, plays a crucial role in decision-making and has recently found application in recommendation systems. In this study, we introduce GcPp, a clustering algorithm that leverages pairwise preference data to generate recommendations for user groups. Initially, we construct individual graphs for each user based on their pairwise preferences and utilize a graph convolutional network to predict similarities between all pairs of graphs. These predicted similarity scores form the foundation of our research. We then construct a new graph where users are nodes and the edges are weighted according to the predicted similarities. Finally, we perform clustering on the graph’s nodes (users). By evaluating various metrics, we found that employing a similarity metric based on a convolutional neural network (SimGNN) with our proposed ground truth called Top-K yielded the highest accuracy. The proposed approach is specifically designed for group recommendation systems and holds significant potential for group decision-making problems. Code is available at https://github.com/RozaAbolghasemi/Group_Recommendation_Syatem_GcPp_clustering.

Survey on Recommender Systems for Biomedical Items in Life and Health Sciences

The generation of biomedical data is of such magnitude that its retrieval and analysis have posed several challenges. A survey of recommender system (RS) approaches in biomedical fields is provided in this analysis, along with a discussion of existing challenges related to large-scale biomedical information retrieval systems. We collect original studies, identify entities and models, and discuss how knowledge graphs (KGs) can improve results. As a result, most of the papers used model-based collaborative filtering algorithms, most of the available datasets did not follow the standard format < user, item, rating >, and regarding qualitative evaluations of RSs use mainly classification metrics. Finally, we have assembled and coded a unique dataset of 60 papers — Sur-RS4BioT, available for download at DOI:10.34740/kaggle/ds/2346894

Matrix factorization with a sigmoid-like loss control

Matrix factorization is one of the fundamental approaches of recommender systems. With the popular L2 loss, learning models tend to overfit significantly deviated predictions. However, predicting the actual rating of 5 as 1 or 2 makes no essential difference in the application. In this paper, we design a sigmoid-like function to control the loss of each individual prediction, which has two advantages. First, it reduces the loss corresponding to significantly deviated predictions. Therefore, the impact of these predictions, some of which may be caused by outliers, is also reduced. Second, it is independent of two classical over-fitting control techniques using regular terms and validation data, respectively. Hence, it can be combined with them to form a more powerful method. Experiments are undertaken on six benchmark datasets in comparison with different losses. Results show that the proposed loss function has good performance in terms of MAE, RMSE, and NDCG, however not so good in terms of HR and MAP.

Embedding Compression in Recommender Systems: A Survey

To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued embeddings. However, the embedding tables are huge and account for most of the parameters in industrial-scale recommender systems. In order to reduce memory costs and improve efficiency, various approaches are proposed to compress the embedding tables. In this survey, we provide a comprehensive review of embedding compression approaches in recommender systems. We first introduce deep learning recommendation models and the basic concept of embedding compression in recommender systems. Subsequently, we systematically organize existing approaches into three categories: low precision, mixed dimension, and weight sharing. Lastly, we summarize the survey with some general suggestions and provide future prospects for this field.

A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously

A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

Recommender system based on Convolutional Recurrent Deep Learning for protein-drug interaction prediction

The tremendous growth the pharmaceutical field witnessed especially concerning drug-development, caused drug-related experiments such as those concerned with estimating their interactions with targets harder to conduct. This issue led researchers to transition from vivo and vitro to selico experimenting methods, which deploy artificial intelligence (AI) techniques such as recommender systems (RS) to solve problems at hand. However, most RSs depend on global features such as indices, which render them incapable of fully understanding the behaviors of individuals. In this paper, we introduce our Convolutional Recurrent Deep Learning model-based RS approach, or ‘RSCRDL’, which tackles the interaction prediction problem from a natural language processing approach’s (NLP) point of view. RSCDRL processes the textual representations of both protein sequences and drugs in order to learn the implicit features of the tokens forming them, rendering it able to process unseen and non-reactive individuals indirectly. We also present a greedier variant of RSCRDL that can exploit additional drugs explicit features for better predictive abilities. Experiments performed on two real-world datasets show significant outperformance achieved by our models over two general-purpose state-of-the-art RSs, while other experiments conducted on orphan protein sequences helped deduce drug candidates that are assumed to be capable of inhibiting their activities.

DOR: a novel dual-observation-based approach for recommendation systems

As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.

A Comprehensive Survey of Recommender Systems Based on Deep Learning

With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.

A Utility-Preserving Obfuscation Approach for YouTube Recommendations

Online content platforms optimize engagement by providing personalized recommendations to their users. These recommendation systems track and profile users to predict relevant content a user is likely interested in. While the personalized recommendations provide utility to users, the tracking and profiling that enables them poses a privacy issue because the platform might infer potentially sensitive user interests. There is increasing interest in building privacy-enhancing obfuscation approaches that do not rely on cooperation from online content platforms. However, existing obfuscation approaches primarily focus on enhancing privacy but at the same time they degrade the utility because obfuscation introduces unrelated recommendations. We design and implement DeHarpo, an obfuscation approach for YouTube's recommendation system that not only obfuscates a user's video watch history to protect privacy but then also denoises the video recommendations by YouTube to preserve their utility. In contrast to prior obfuscation approaches, DeHarpo adds a denoiser that makes use of a ``secret'' input (i.e., a user's actual watch history) as well as information that is also available to the adversarial recommendation system (i.e., obfuscated watch history and corresponding ``nois`` recommendations). Our large-scale evaluation of DeHarpo shows that it outperforms the state-of-the-art by a factor of 2x in terms of preserving utility for the same level of privacy, while maintaining stealthiness and robustness to de-obfuscation.

Extended Collaborative Filtering Recommendation System with Adaptive KNN and SVD

In recent years, recommendation systems have gained significant importance due to the vast amount of digital content available on various online platforms. Collaborative filtering is a widely adopted approach in recommendation systems, leveraging user-item interactions to make personalized predictions. However, traditional collaborative filtering methods face challenges such as the cold-start problem and data sparsity. To address these issues, researchers have proposed advanced techniques, including Adaptive KNN-Based and SVD-Based Extended Collaborative Filtering. This paper provides a comprehensive review of these two recommendation systems, discussing their underlying principles, advantages, and limitations. Furthermore, we explore recent research advancements and real-world applications, providing insights into the potential future developments in this field.

A distributed real-time recommender system for big data streams

Recommender Systems (RS) play a crucial role in our lives. As users become continuously connected to the internet, they are less tolerant of obsolete recommendations made by an RS. Online RS has to address three requirements: continuous training and recommendation, handling concept drifts, and the ability to scale. Streaming RS proposed in the literature address the first two requirements only. That is because they run the training process on a single machine. To tackle the third challenge, we propose a Splitting and Replication mechanism for distributed streaming RS. Our mechanism is inspired by the shared-nothing architecture that underpins contemporary big data processing systems. We have applied our mechanism to two well-known approaches for online RS, namely, matrix factorization and item-based collaborative filtering. We conducted experiments comparing the performance with the baseline (single machine). Evaluating different data sets, experiments show online recall improvement by 40% with more than 50% less memory consumption.

Advanced weighted hybridized approach for recommendation system

PurposeThis paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application.Design/methodology/approachIt is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application.FindingsThis paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system.Originality/valueThe proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git.

Hybrid approach for next basket recommendation system

Hybrid approach for next basket recommendation system

A multi-objective artificial bee colony approach for profit-aware recommender systems

Movie recommender systems are increasingly present in our daily lives, offering content of interest from streaming providers. Objectives in addition to the liking probability can be proposed to provide movie recommendations. However, there is a lack of recommenders that are aware of the benefit and that address the multi-objective nature of the problem. A profit-aware recommender system based on swarm intelligence in a multi-objective environment (multi-objective artificial bee colony, MOABC) has been designed, implemented, and applied. The proposed approach incorporates new intelligent operators that try to improve both objectives (liking probability and profit) simultaneously in each iteration instead of exclusively using randomness. This increases the quality of the recommendations with respect to the state-of-the-art algorithms. This new proposal has been evaluated using MovieLens datasets, covering different sizes (large, medium, and small). The experiments show that the MOABC performs better than collaborative filtering (CF, a standard in recommender systems) and Non-dominated Sorting Genetic Algorithm II (NSGA-II, the only multi-objective proposal in scientific literature that is profit aware) in terms of accuracy and global profit. Furthermore, statistical analysis shows that the proposed approach generates better and more robust results, also showing that the multi-objective nature of the problem must be exploited.

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners’ learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners’ current skill level and learning goals outperforms the other approaches in practice problem recommender systems.

A MinMax Item-based Method for Multi-Criteria Recommendation Systems

One of the common challenges in multi-criteria recommendation systems is dealing with data normalization. The challenge occurs since criteria can have diverse rating ranges and user rating behaviors are dissimilar. Previous studies on data normalization showed the supremacy of the Decoupling technique in the user-based multi-criteria recommendation system and the MinMax technique in the multi-criteria decision-making system as well as in data mining. A study also showed that the performance of Decoupling is improved in the item-based method than in the user-based. However, no study has been conducted to investigate the performance of MinMax compared to Decoupling in the item-based multi-criteria recommendation system. This study aims to combine the MinMax normalization technique and the item-based modeling approach in a multi-criteria recommendation system. The proposed method is named the MinMax Item-based method (MIB). We conducted a series of experiments using the Yelp Hotel multi-criteria rating dataset to perform a sensitivity analysis of MIB. The best settings are then used to benchmark MIB MIB towards DCMItem, i.e., a method that combines the Decoupling normalization technique and item-based multi-criteria modeling approach. The comparison results show the outperformance of MIB towards DCMItem by 2.30% in Precision and 2.00% in NDCG. Therefore, we can conclude that MixMax is able to improve the performance of the item-based multi-criteria recommendation system better than Decoupling.