In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative fil...
Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal informat...
Recently, game companies have been increasingly offering a variety of content in their games. The more this happens, the more players will need to consider what is best for them. Players who have played such a game may not find it difficult to play,...
Recommendation system plays an indispensable role in helping users make decisions in different application scenarios. The issue about how to improve the accuracy of a recommendation system has gained widespread concern in both academic and industry f...
Wallboard outsourcing is a critical task in cloud-based manufacturing, where demand enterprises seek suitable suppliers for machining services through online platforms. However, the recommendation process faces significant challenges, including spars...
E-learning is an innovative strategy for enhancing teaching and learning in digital environments with the goal of enhancing education. In the same context, recommendation models have been developed for predicting the user’s learning preferences...
The drastic increase of websites is one of the causes behind the recent information overload on the internet. A recommender system (RS) has been developed for helping users filter information. However, the cold-start and sparsity problems lead to low...
This study considers the team recommendation problem as a generalized assignment problem. Firstly, a formal description of the team recommendation problem is given; secondly, a team-recommended generalized assignment model (TRGAM) is established base...
Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the...
Music, movies, books, pictures, and other media can change a user’s emotions, which are important factors in recommending appropriate items. As users’ emotions change over time, the content they select may vary accordingly. Existing emoti...
The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommen...
Compared with traditional point-of-interest (POI) recommendation, next POI recommendation is more difficult and requires comprehensive consideration of users’ behavior patterns, spatial–temporal context, and other information. In addition...
With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instruction...
Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing sessio...
In recent years, recommendation systems have made significant strides through the application of graph neural networks (GNNs). However, most of the existing methods primarily focus on modeling user–item interactions, often failing to account fo...
Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating stan...
As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essentia...
There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and pr...
Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more interesting unknown locations and facilitate service providers to provide users with more accurate...
The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs)....
The issue of congestion on urban roads stems from an imbalance between transport demand and supply. It has become imperative to address the problem from the traffic demand side. While managing effective traffic demand relies on understanding the indi...
A recommendation method based on heterogeneous information networks and multiple trust relationships is proposed. Firstly, the node sequence in the heterogeneous information network is obtained through the random walk of the meta-path, and the repres...
In view of the problem that the recommendation system is not good at integrating multi-source information and user sentiment, this paper proposes a BERT-LSTM Dual-Tower Recommendation Method for Sequential Feature Extraction (BLDRM-SFE). This method...
The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we fo...
In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with...
Traditional collaborative filtering recommendation algorithms only consider the interaction between users and items leading to low recommendation accuracy. Aiming to solve this problem, a graph convolution collaborative filtering recommendation metho...
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to ses...
Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of us...
In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interes...
With the development of the digital and intelligent transformation of the power grid, the structure and operation and maintenance technology of the power grid are constantly updated, which leads to problems such as difficulties in information acquisi...
The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profi...
Knowledge graphs have shown great potential in alleviating the data sparsity problem in recommendation systems. However, existing graph-attention-based recommendation methods primarily focus on user–item–entity interactions, overlooking p...
Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation s...
Personalized news recommendations focus on providing users with news that fits their interests and alleviates their information overload. User preference modeling is crucial for achieving personalized news recommendations, and user preferences are us...
The rapid increase in the number of new maize varieties and the intensification of market competition have raised the need to precisely promote new maize varieties to suitable planting areas and fully exploit the variety potential and win the market...
The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended...
Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system developme...
The strong interactivity and size limitation of the mobile interface calls for the utilization of users’ aesthetic preferences to provide better mobile marketing recommendations in order to promote the sustainable development of m-commerce. Exi...
Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid grow...
Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraint...
There are no restrictions on religious or cultural practices in India. India’s temples are becoming an ideal platform for Hindu groups to express their ideals in a global context. For the sake of devotees, temples now require widespread accessi...
To address the challenges of user behavior sparsity and insufficient utilization of course semantics on MOOC platforms, this paper proposes a personalized recommendation method that integrates user behavioral sequences with course textual semantic fe...
Point-of-interest (POI) recommendation is the prevalent personalized service in location-based social networks (LBSNs). A single use of matrix factorization (MF) or deep neural networks cannot effectively capture the complex structure of user–P...
It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information...
Exploring food’s rich composition and nutritional information is crucial for understanding and improving people’s dietary preferences and health habits. However, most existing food recommendation models tend to overlook the impact of food...
The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investiga...
In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection networ...
The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different...
The objective of cross-city recommendation is to suggest points-of-interest (POI) in the target city that may be of interest to users, based on their check-in records from their source city. Although significant progress has been made in studying use...
Given that existing methods for recommending electricity sale packages primarily consider scenarios where customers are familiar with all package attributes, they overlook psychological factors, attribute correlations, and the determination of attrib...
of 426