Recommender Systems
Frontiers and Practices
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617 Result(s)
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**ya Lu
Data Mining and Knowledge Discovery
Information Storage and Retrieval
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Article
An overview of semantic-based process mining techniques: trends and future directions
Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or enhancement. Domain experts have developed several process mining al...
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Article
Improving graph-based recommendation with unraveled graph learning
Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph Neural Networks. Furthermore, the integration of contrastive lea...
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Article
A new neighbourhood-based diffusion algorithm for personalized recommendation
Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user...
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Article
C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector
Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by r...
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Article
BotCL: a social bot detection model based on graph contrastive learning
The proliferation of social bots on social networks presents significant challenges to network security due to their malicious activities. While graph neural network models have shown promise in detecting soci...
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Article
Adaptive semi-supervised learning from stronger augmentation transformations of discrete text information
Semi-supervised learning is a promising approach to dealing with the problem of insufficient labeled data. Recent methods grouped into paradigms of consistency regularization and pseudo-labeling have outstandi...
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Article
Online concept evolution detection based on active learning
Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient to reflect the training data distribution, it will often furt...
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Article
An anomaly aware network embedding framework for unsupervised anomalous link detection
Most existing network embedding based anomalous link detection methods regard network embedding and anomalous link detection as two independent tasks. However, removing anomalous links from the original networ...
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Chapter and Conference Paper
VEMO: A Versatile Elastic Multi-modal Model for Search-Oriented Multi-task Learning
Cross-modal search is one fundamental task in multi-modal learning, but there is hardly any work that aims to solve multiple cross-modal search tasks at once. In this work, we propose a novel Versatile Elastic Mu...
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Chapter and Conference Paper
A Comprehensive Review of Relation Prediction Techniques in Knowledge Graph
Knowledge graphs organize entity relations using a graph structure, facilitating knowledge representation. In research, relation prediction within knowledge graphs plays a crucial role, aiding inference, laten...
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Chapter
Summary and Outlook
This chapter provides a summary of the book and offers insights into future trends in the research and application of recommender systems.
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Chapter
Classic Recommendation Algorithms
This chapter introduces four types of classic recommendation algorithms, including content-based recommendation algorithms, classic collaborative filtering algorithms, matrix factorization methods, and factori...
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Chapter
Deep Learning-Based Recommendation Algorithms
This chapter introduces the relationship between collaborative filtering and deep learning and then presented various deep learning-based collaborative filtering algorithms. Leveraging cutting-edge methods fro...
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Chapter
Practical Recommender System
This chapter focuses on some problems and considerations in industry applications of recommender systems, and discusses the details of these actual applications based on the code in the Microsoft Recommenders ...
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Book
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Chapter
Overview of Recommender Systems
This chapter first introduces the history of the recommender system and the revolutionary changes in the field of recommender systems. Then, this chapter introduces the basic principles of recommender systems,...
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Chapter
Foundations of Deep Learning
This chapter introduces the basics of deep learning, including feedforward computation and backpropagation algorithms for deep neural networks, as well as various classic neural network models. As readers lear...
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Chapter
Recommender System Frontier Topics
This chapter introduces the hotspots of recommender system research, the key challenges of recommender system application, and how to achieve responsible recommendation technically. These contents may become t...
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Chapter and Conference Paper
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, thi...
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Article
Fuzzy clustering analysis for the loan audit short texts
In China, post-loan management is usually executed in the form of a visit survey conducted by a credit manager. Through a quarterly visit survey, a large number of loan audit short texts, which contain valuabl...
