THE STATE OF THE ART IN ADAPTIVE INFORMATION AGENTS

2002 ◽  
Vol 11 (01) ◽  
pp. 19-61 ◽  
Author(s):  
RAYMOND Y. K. LAU
Keyword(s):  
Exponential Growth ◽  
Information Overload ◽  
State Of The Art ◽  
Explanatory Power ◽  
Representation Learning ◽  
The Internet ◽  
Document Representation ◽  
Information Agents ◽  
Internet Information ◽  
Pros And Cons

With the exponential growth of the Internet, information seekers are faced with the so-called problem of information overload. Adaptive Information Agents have been developed to alleviate this problem. The Main issues in the development of these agents are document representation, learning, and classification. Various paradigms have been explored for the development of adaptive information agents, and the performance of these agents differs in terms of computational efficiency, classification effectiveness, learning autonomy, exploration capability, and explanatory power. To develop a basic under-standing of the pros and cons of these paradigms, some representative information agents are examined. Such a review also serves to identify a general for the development of the next generation adaptive information agents.

User profiling in personal information agents: a survey

2005 ◽  
Vol 20 (4) ◽  
pp. 329-361 ◽  
Author(s):  
DANIELA GODOY ◽  
ANALIA AMANDI
Keyword(s):  
State Of The Art ◽  
Personal Information ◽  
Relevant Information ◽  
User Profiling ◽  
The Internet ◽  
User Profiles ◽  
Amount Of Information ◽  
Information Agents ◽  
Personal Agents ◽  
Intelligent Information

Personal information agents have emerged in the last decade to help users to cope with the increasing amount of information available on the Internet. These agents are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context of intelligent information agents. Existing approaches and lines of research in the main dimensions of user profiling, such as acquisition, learning, adaptation and evaluation, are discussed.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

Algorithm Study and Application of Personalized Intelligent Recommendation Engine

2014 ◽  
Vol 989-994 ◽  
pp. 1445-1448
Author(s):  
Guo Lin Pu
Keyword(s):  
Information Overload ◽  
Personalized Recommendation ◽  
The Internet ◽  
Internet Information ◽  
Internet Development

The Internet has gradually enters the life of people and the usage of information has become globalization gradually. It is now very common to get information through the network in day-to-day work. The Amount of data of Internet information grows in an explosive way in recent years, the huge amounts of information make it more and more difficult for users to find the information they need accurately; information overload lots problems become the great challenge of Internet development. As currently one of the most effective tool to solve the information overload, personalized recommendation technology help users filter information intelligently through the recommendation engine.

scholarly journals Movie Recommendation System using Cosine Similarity and KNN

2020 ◽  
Vol 9 (5) ◽  
pp. 556-559
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Vital Role ◽  
The Internet ◽  
Learning Approaches ◽  
Meaningful Information ◽  
The Past ◽  
Pros And Cons ◽  
Movie Recommendation ◽  
Insight Into

Over the past years, the internet has broadened the horizon of various domains to interact and share meaningful information. As it is said that everything has its pros and cons therefore, along with the expansion of domain comes information overload and difficulty in extraction of data. To overcome this problem the recommendation system plays a vital role. It is used to enhance the user experience by giving fast and coherent suggestions. This paper describes an approach which offers generalized recommendations to every user, based on movie popularity and/or genre. Content-Based Recommender System is implemented using various deep learning approaches. This paper also gives an insight into problems which are faced in content-based recommendation system and we have made an effort to rectify them.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

scholarly journals Efficient Graph Collaborative Filtering via Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4666
Author(s):  
Zhiqiang Pan ◽  
Honghui Chen
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Representation Learning ◽  
First Order ◽  
Satisfactory Performance ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Bayesian Personalized Ranking ◽  
Graph Neural Networks ◽  
Training Efficiency

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

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