A Survey on the Scalability of Recommender Systems for Social Networks

Sardianos, Christos; Tsirakis, Nikos; Varlamis, Iraklis

doi:10.1007/978-3-319-90059-9_5

Cited by 13 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to solve the technical issues that may arise from the scalability of recommender systems in large datasets, several parallel and distributed algorithms have been proposed, which either rely on the splitting of the dataset, using social or other information [205,206] and its parallel processing, or on the refactoring of existing algorithms in order to take advantage of the use of graphical processing units (GPUs) [207,208,209]. The issue of big data handling has also been studied in the domain of energy efficient recommender systems for recommending energy plans [210], providing actionable recommendations [157] or improving comfort and energy efficiency in tandem [211].…”

Section: Large-scale Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

show abstract

Section: Large-scale Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the digital era, personalized recommendation systems have become a crucial bridge connecting users with vast amounts of information [1]. Accurately capturing users' long-term and short-term interests and effectively distinguishing between genuine interests and the conformity effect influenced by social factors to correct biases and enhance recommendation accuracy has become a key focus for personalized recommendation [2].…”

Section: Introductionmentioning

confidence: 99%

Debiased Recommendation Based on Comparative Learning and Causal Embedding

Liu,

Xun,

et al. 2024

Preprint

View full text Add to dashboard Cite

Biases in recommendation systems significantly reduce recommendation accuracy and user experience. To address the issues in traditional recommendation systems: (1) inaccurate recommendation results due to ineffective modeling of users’ long-term and short-term interests, (2) popularity bias caused by the conformity effect, a debias recommendation method based on contrastive learning and causal embedding (CLACE). CLACE first employs two independent encoders to model users’ long-term and short-term interests separately. Then, a contrastive learning framework is designed to supervise the similarity between the long-term and short-term interest representations and interest proxies. A dynamic attention mechanism is introduced to adaptively adjust the weights of long-term and short-term interests to accurately reflect users’ preference biases. Simultaneously, to fundamentally reduce the popularity bias induced by the conformity effect during the recommendation process, a causal embedding model is introduced to separate user interests from the conformity effect, eliminating the negative impact of the conformity effect on recommendation results and achieving more precise recommendations. The effectiveness of the proposed CLACE is validated on two public datasets, and experimental results demonstrate that CLACE significantly improves recommendation accuracy, recall, and normalized discounted cumulative gain.

show abstract

“…User product views and actions relate to implicit (model-based) category. The features such as Likes and follows are associated with an explicit category (memory-based) [8][9][10][11]. The contemporary state-of-the-art on the CF recommendation system has reported classification and prediction of future items to be bought with good accuracy.…”

Section: Introductionmentioning

confidence: 99%

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

et al. 2020

View full text Add to dashboard Cite

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

show abstract

A Survey on the Scalability of Recommender Systems for Social Networks

Cited by 13 publications

References 46 publications

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Debiased Recommendation Based on Comparative Learning and Causal Embedding

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Contact Info

Product

Resources

About