Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to use contrastive loss to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views of the same ...

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared ...

Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by ...

The cross-domain recommender systems aim to alleviate the data sparsity problem in the target domain by transferring knowledge from the auxiliary domain. However, existing works ignore the fact that the data sparsity problem may also exist in the single ...

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political ...

Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds ...

A cross-domain recommendation system is an intelligent recommendation technology that integrates multiple fields or types of data. It can cross independent information islands, effectively integrate and complement data resources, and improve ...

A filtering method is indispensable in a data-flooded environment. Recommended systems have made a massive step towards this aim, speeding up internet-based customer experience. Most of today's examples of artificial marketing intelligence are ...

Loop-based intelligent tutoring systems (ITSs) support the learning process using a step-by-step problem-solving approach. A limitation of ITSs is that few contents are compatible with this approach. On the other hand, recommendation systems can ...

In order to overcome the problems of low accuracy and long processing time of personalised recommendation of educational resources in traditional personalised recommendation methods of educational resources, a personalised recommendation method of ...

Day to day, the uploading of data into the world wide web and e-commerce directed the development of recommender systems. Recommender system filters the information based on the user's interest. Nowadays, recommender systems are being used in every ...

Nowadays, e-commerce has become one of the most popular shopping ways for worldwide customers especially after the outbreak of COVID-19 worldwide. To aid the scientific shopping decision-makings of customers, collaborative filtering is often used to ...

There are evident sequential patterns in human visits to different locations in daily life, which has led to the emergence of numerous sequence-based geographic location recommendation methods. However, previous research has mainly focused on ...$$$$

Nowadays, the personalized recommendation has become a research hotspot for addressing information overload. Despite this, generating effective recommendations from sparse data remains a challenge. Recently, auxiliary information has been widely ...

Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user–item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate ...

The security of recommendation systems with collaborative filtering from manipulation attacks is considered. The most common types of attacks are analyzed and identified. A modified method for detecting manipulation attacks on recommendation ...

Recommender systems are special algorithms that allow users to receive personalized recommendations on topics that interest them. Systems of this kind are widely used in various fields, for example, in e-commerce, provider services, social ...

The customer experience in e-commerce can be enhanced by the employment of strong recommender systems. They can help customers identify items that interest them and also help to increase sales. A recommender system type that is gaining popularity is the ...

In the contemporary era of big data, recommendation systems play a crucial role in guiding our daily decision-making amidst an overwhelming array of choices. Personalized recommendations have become increasingly popular by tailoring suggestions to user ...

With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as the ...