Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

scholarly journals Mobile Recommendation Based on Link Community Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Kun Deng ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Mobile Networks ◽  
Real World ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Greedy Search ◽  
Recommendation Algorithm

Since traditional mobile recommendation systems have difficulty in acquiring complete and accurate user information in mobile networks, the accuracy of recommendation is not high. In order to solve this problem, this paper proposes a novel mobile recommendation algorithm based on link community detection (MRLD). MRLD executes link label diffusion algorithm and maximal extended modularity (EQ) of greedy search to obtain the link community structure, and overlapping nodes belonging analysis (ONBA) is adopted to adjust the overlapping nodes in order to get the more accurate community structure. MRLD is tested on both synthetic and real-world networks, and the experimental results show that our approach is valid and feasible.

scholarly journals Estimating Latent People Flow without Tracking Individuals

2018 ◽  
Author(s):  
Yusuke Tanaka ◽  
Tomoharu Iwata ◽  
Takeshi Kurashima ◽  
Hiroyuki Toda ◽  
Naonori Ueda
Keyword(s):  
New York ◽  
New York City ◽  
Real World ◽  
Diffusion Processes ◽  
Temporal Dynamics ◽  
Location Information ◽  
Aggregated Data ◽  
Proposed Model ◽  
Real World Datasets ◽  
Flow Conservation

Analyzing people flows is important for better navigation and location-based advertising. Since the location information of people is often aggregated for protecting privacy, it is not straightforward to estimate transition populations between locations from aggregated data. Here, aggregated data are incoming and outgoing people counts at each location; they do not contain tracking information of individuals. This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data. With the proposed model, temporal dynamics of people flows are assumed to be probabilistic diffusion processes over a network, where nodes are locations and edges are paths between locations. By maximizing the likelihood with flow conservation constraints that incorporate travel duration distributions between locations, our model can robustly estimate transition populations between locations. The statistically significant improvement of our model is demonstrated using real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

scholarly journals A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 17 ◽  
Author(s):  
Emmanuel Pintelas ◽  
Ioannis E. Livieris ◽  
Panagiotis Pintelas
Keyword(s):  
Machine Learning ◽  
Real World ◽  
High Performance ◽  
Black Box ◽  
Box Model ◽  
Proposed Model ◽  
Wide Range ◽  
Critical Issues ◽  
Key Factor ◽  
Real World Datasets

Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.

A Collaborative Filtering Recommendation Algorithm Based on User Clustering in E-Commerce Personalized Systems

2011 ◽  
Vol 267 ◽  
pp. 789-793 ◽  
Author(s):  
Guang Hua Cheng
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbors ◽  
Recommendation Algorithm ◽  
Active User ◽  
User Clustering ◽  
Target User ◽  
Service Efficiency ◽  
Collaborative Filtering Algorithm ◽  
Two Stages ◽  
Successful Technology

Electronic commerce recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is the most successful technology for building recommendation systems. Unfortunately, the efficiency of this method declines linearly with the number of users and items. So, as the magnitudes of users and items grow rapidly, the result in the difficulty of the speed bottleneck of collaborative filtering systems. In order to raise service efficiency of the personalized systems, a collaborative filtering recommendation method based on clustering of users is presented. Users are clustered based on users ratings on items, then the nearest neighbors of target user can be found in the user clusters most similar to the target user. Based on the algorithm, the collaborative filtering algorithm should be divided into two stages, and it separates the procedure of recommendation into offline and online phases. In the offline phase, the basic users are clustered into centers; while in the online phase, the nearest neighbors of an active user are found according to the basic users’ cluster centers, and the recommendation to the active user is produced.

scholarly journals Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2510
Author(s):  
Aaron Ling Chi Yi ◽  
Dae-Ki Kang
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Real World ◽  
Experimental Analysis ◽  
Location Awareness ◽  
Research Communities ◽  
Current Location ◽  
Real World Datasets ◽  
Filtering Approach

Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.

A Novel Personalized Filtering Recommendation Algorithm Based on Collaborative Tagging

2011 ◽  
Vol 186 ◽  
pp. 621-625
Author(s):  
Ming Yang Sun ◽  
Wei Feng Sun ◽  
Xi Dong Liu ◽  
Lei Xue
Keyword(s):  
Computational Complexity ◽  
Collaborative Filtering ◽  
Collaborative Tagging ◽  
Recommendation Algorithm ◽  
Trade Off ◽  
Filtering Method ◽  
Recommendation Algorithms ◽  
Sparsity Level ◽  
Personal Interests

Recommendation algorithms suffer the quality from the huge and sparse dataset. Memory-based collaborative filtering method has addressed the problem of sparsity by predicting unrated values. However, this method increases the computational complexity, sparsity and expensive complexity of computation are trade-off. In this paper, we propose a novel personalized filtering (PF) recommendation algorithm based on collaborative tagging, which weights the feature of tags that show latent personal interests and constructs a top-N tags set to filter out the undersized and dense dataset. The PF recommendation algorithm can track the changes of personal interests, which is an untilled field for previous studies. The results of empirical experiments show that the sparsity level of PF recommendation algorithm is much lower, and it is more computationally economic than previous algorithms.

scholarly journals Overlapping Community Detection Based on Membership Degree Propagation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 15
Author(s):  
Rui Gao ◽  
Shoufeng Li ◽  
Xiaohu Shi ◽  
Yanchun Liang ◽  
Dong Xu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Computational Time ◽  
Membership Degree ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Real World Datasets

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.

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