The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, th...
Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news a...
News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Pe...
News recommendation is a critical technology that helps users efficiently find content of interest from large candidate pools. Its core objective is to accurately model user reading interests. However, current news recommendation systems typically su...
With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast am...
Currently, abnormality detection and/or prediction is a very hot topic. In this paper, we addressed it in the frame of activity monitoring of a human in bed. This paper presents a comprehensive formulation of a requirements engineering dossier for a...
This paper proposes a model to predict users’ personal interests from their gaze. Current image processing technologies enable us to identify each user’s gaze and pupil diameter. However, they cannot identify user interests. The model use...
Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, whic...
In recent years, the proposed Deep Interest Network (DIN), Deep Interest Evolution Network (DIEN) and Deep Session Interest Network (DSIN) have further developed click-through rate prediction models. The above three models mainly focus on the evoluti...
The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter...
Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music accord...
Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established vario...
In recent years, graph neural networks (GNNs) have been widely applied in recommendation systems. However, most existing GNN models do not fully consider the complex relationships between heterogeneous nodes and ignore the high-order semantic informa...
Though recommendation systems can help users save time while shopping online, their performance is significantly limited by sparse user data and the inability to capture temporal dynamics of user preferences, such as interest forgetting and topic evo...
The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information...
In this paper, we propose a recommendation system for social media users which makes recommendations on the basis of the user profile information and the contents posted by users-the bio-aware algorithm. Text mining techniques are used to pre-process...
Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search and e-commerce). Existing recommendation alg...
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of leng...
The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the re...
Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the comp...
Personalized search and recommendation algorithms for multi-modal data have attracted widespread attention. However, existing methods often struggle with effectively integrating multi-source information and performing global search in complex optimiz...
22 August 2023
The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieve...
The crucial role of urban industrial land redevelopment in sustainable urban renewal has garnered widespread attention. While some scholars have explored the interest game among stakeholders in industrial land redevelopment, they primarily focus on t...
With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) dom...
Personality accounts for how individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles. Personality, especially in the form of the Five Factor Model, has shown usefulness in personalized systems,...
How to find a user’s interest from similar users a fundamental research problems in socialized recommender systems. Despite significant advances, there exists diversity loss for the majority of recommender systems. With this paper, for expandin...
Over the years, the purpose of cultural heritage (CH) sites (e.g., museums) has focused on providing personalized services to different users, with the main goal of adapting those services to the visitors’ personal traits, goals, and interests....
Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users...
With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and dis...
The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users’ behavioral patterns and improve the accuracy of location-based services, poin...
To find useful information on the Web, a user must define the search according to their interests, then they must select and analyze one or more web pages, and finally they must decide which content is most useful to them. This process requires visua...
With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical che...
Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also una...
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 rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue...
This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative rese...
Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have ini...
Influence maximization is a crucial research domain in social network analysis, playing a vital role in optimizing information dissemination and managing online public opinion. Traditional IM models focus on network topology, often overlooking user h...
Social networks open up a new channel of video sharing and promote the scale and efficiency of video diffusion. Adjustable and scalable video diffusion is significant for the quality and performance of the service of video systems. In this paper, we...
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of...
Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model...
The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user’s next...
Graph convolutional networks (GCNs) show great potential in recommendation applications, as they have excellent performance in propagation node information propagation and capturing high-order connectivity in user-item interaction graphs. However, in...
Current information exploration models present low-level features or technical aspects related to the paradigm used to generate results. While this may increase transparency, it does not help the user form a personal opinion about items because it do...
1 September 2017
Channelizing product sales with the aid of Recommender Systems is ubiquitous in e-commerce firms. Recommender systems help consumers by reducing their search cost by directing them to interesting and useful products. It also helps e commerce firms by...
People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and sema...
The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendat...
The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible a...
Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to th...
Users of utility tunnel public–private partnership (PPP) projects are productive corporate entities with a specific scope, a limited quantity, and strong bargaining power. They possess the ability and motivation to participate in PPP projects t...
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