Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues.

/pdf/hcof-hybrid-collaborative-filtering-using-social-and-wembuf7t.pdf

HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a “winner”: the most preferable variant of our hybrid recommender algorithm is one using a ⟨four elicitation slates, six shown images per slate⟩ pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process.

/pdf/a-novel-hybrid-recommender-system-for-the-tourism-domain-1598i205.pdf

A Novel Hybrid Recommender System for the Tourism Domain

It is our great pleasure to welcome you to the 13th ACM Symposium on Access Control Models and Technologies (SACMAT 2008). This year's symposium continues its tradition of being the premier forum for presentation of research results on leading edge issues of access control, including models, systems, applications, and theory.

The call for papers attracted 79 submissions from 24 countries in Africa, Asia, Australia, Europe, North America, and South America. Each submission was reviewed by at least three reviewers who are experts in the field. Furthermore, extensive online discussions took place in making the selections for the symposium. The program committee selected 20 papers that cover a variety of topics, including role mining, workflow access control, access control in distributed environments, policy analysis and visualization, implementations issues in role based access control, usage control, obligations, integrity measurement, and attestation. The program also includes a keynote address and two panels.

Proceedings of the 13th ACM symposium on Access control models and technologies

An enhanced recommender system based on heterogeneous graph link prediction

Machine learning-based new approach to films review

Abstract Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system. 

/pdf/a-systematic-review-and-research-perspective-on-recommender-1elyl9a4.pdf

A systematic review and research perspective on recommender systems

https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-022-00592-5

Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.

An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems

Various conditions that affect the muscles, blood arteries, heart, valves, or internal electrical pathways that regulate muscle contraction are referred to as "heart diseases." This work propose a new heart disease prediction model. The three main steps of the suggested framework are feature extraction, proposed dimensionality reduction, and proposed optimal ensemble-based heart disease prediction. Here, the most crucial components of the collected input data were extracted first. For accurate prediction, a significant number of features have been retrieved. Using Normalized Mutual Information induced Principal Component Analysis (NM-PCA), a novel feature dimension reduction technique has been proposed to overcome these problems. The dimensionally reduced features obtained by NM-PCA are simultaneously trained by the SVM, RF, and KNN classifiers. The improved optimized RNN generates the final prediction result. This optimized RNN is trained using the relevant outputs from SVM, RF, and KNN. Deer Updated Moth flame Optimization is used to adjust the weight function of the optimized RNN since it makes the ultimate judgment (DUMFO). After utilizing DUMFO, the RNN's prediction accuracy has greatly increased. The Deer Hunting Optimization Algorithm (DHO) and the Moth Flame Optimization (MFO) algorithms are combined to generate the DUMFO. Finally, using both positive and negative criteria, the performance of the suggested methodology is compared to existing, methodologies. The accuracy of the projected model is 47.3%, 45.2%, 57.8%, 36.8%, 26.3%, 14.7%, 16.8%, 5.12% and 3.15% superior to existing models like DBN, NN, CNN, LSTM, PM-LU, GA, SMO+EC, SSO+EC, BOA+EC, WOA+EC, MFO+EC and DHOA+EC, respectively. 

A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

Group Recommender Systems (GRS) have recently gained attention for use in various applications. The GRS model is useful in any scenario that involves a decision-making process for a group of users. However, existing approaches are not able to provide high quality recommendations due to their inability to handle traditional problems such as cold start, sparsity, and dealing with large dimensional information. To address these issues, a novel deep learning-based adaptive model is proposed. Raw data is first collected from four different data sources and cleaned using stemming, stop word and punctuation removal methods. Data preprocessing is followed by the feature extraction phase. A Text Convolutional Neural Network (TCNN) is employed to extract efficient features from group reviews. The efficient features are fed into an Adaptive Deep Belief Network (ADBN) to provide the recommendation output. It is further optimized using the Henry Gas Solubility optimization (HGSO) algorithm. Through extensive experiments it is found that the performance of the proposed system outperforms existing standard algorithms, hence, solving the traditional GRS issues.

An Intelligent Group Recommender System using Henry Gas Solubility Optimization Algorithm-based Adaptive Deep Belief Network

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A systematic review and research perspective on recommender systems

A systematic review and research perspective on recommender systems

An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems

A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

An Intelligent Group Recommender System using Henry Gas Solubility Optimization Algorithm-based Adaptive Deep Belief Network