A comparative study of matrix factorization and random walk with restart in recommender systems

Park, Haekyu; Jung, Jinhong; Kang, U

doi:10.1109/bigdata.2017.8257991

Cited by 29 publications

(19 citation statements)

References 26 publications

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“…The transition matrix is defined based on the used graph structure. The value of restarting probability (β) is considered to be 0.2 [43]. For each user u a , the relevance scores of unvisited locations m j , j = 1, 2, ..., M are obtained as:…”

Section: Group Recommendations By the Rwr Under Aggregated Predictionmentioning

confidence: 99%

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Khazaei

Alimohammadi

2019

IJGI

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Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

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Section: Group Recommendations By the Rwr Under Aggregated Predictionmentioning

confidence: 99%

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Khazaei

Alimohammadi

2019

IJGI

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“…However, they are more complex and time-consuming. Furthermore, most of the recent model-based CF methods [11], [16] focus on adding additional domain information including context, time and location to improve the accuracy of QoS prediction. Although such additional information can improve the predicting accuracy, those models all use simple random sampling method to obtain training data from the original datasets, which makes the training data biased and leads to poor prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Research on the Influence of Sampling Methods for the Accuracy of Web Services QoS Prediction

Jian

2019

IEEE Access

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In recent years, as the number of Web services, increases dramatically, the personalized Web service recommendation has become a hot topic in both academia and industry. The quality-of-service (QoS) prediction plays a key role in Web service recommendation systems. However, how to further improve the accuracy of QoS prediction is still a problem. Traditional QoS predicting models do not consider the impact of sampling methods on the accuracy of QoS prediction. However, the outstanding sampling method can train the predicting model more effectively and obtain higher accuracy. Therefore, it is necessary to study sampling methods based on the QoS dataset in order to obtain sample distribution closer to the original distribution, so as to improve the accuracy of the predicting models. In this paper, we first discuss how to apply several existing sampling methods to QoS datasets and then analyze their advantages and disadvantages. Finally, a novel sampling method, enhanced importance resampling (EIRS), is proposed and applied. The experiments on the real-world datasets show that our method can not only sample efficiently and accurately but also can greatly improve the accuracy of Web service QoS prediction. INDEX TERMS Quality of service, Web services, sampling methods, enhanced importance resampling.

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“…Among numerous methods [9,15,17] to measure the similarity, random walk with restart (RWR) [17] has aroused considerable attention due to its ability to account for the global network structure from a particular user's point of view. RWR has been widely used in various applications across different domains including ranking, link prediction [11], and recommendation [18]. To avoid expensive costs incurred by RWR computation, various methods have been proposed to calculate RWR scores efficiently, and the majority of them have focused on static graphs [12-14, 19, 23].…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Yoon

Jin

Kang

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Restart (RWR) is a popular measure to estimate the similarity between nodes and has been exploited in numerous applications. Many real-world graphs are dynamic with frequent insertion/deletion of edges; thus, tracking RWR scores on dynamic graphs in an efficient way has aroused much interest among data mining researchers. Recently, dynamic RWR models based on the propagation of scores across a given graph have been proposed, and have succeeded in outperforming previous other approaches to compute RWR dynamically. However, those models fail to guarantee exactness and convergence time for updating RWR in a generalized form.In this paper, we propose OSP, a fast and accurate algorithm for computing dynamic RWR with insertion/deletion of nodes/edges in a directed/undirected graph. When the graph is updated, OSP first calculates offset scores around the modified edges, propagates the offset scores across the updated graph, and then merges them with the current RWR scores to get updated RWR scores. We prove the exactness of OSP and introduce OSP-T, a version of OSP which regulates a trade-off between accuracy and computation time by using error tolerance ϵ. Given restart probability c, OSP-T guarantees to return RWR scores with O(ϵ/c) error in O(log (1−c) ( ϵ 2 )) iterations. Through extensive experiments, we show that OSP tracks RWR exactly up to 4605× faster than existing static RWR method on dynamic graphs, and OSP-T requires up to 15× less time with 730× lower L1 norm error and 3.3× lower rank error than other state-of-the-art dynamic RWR methods.

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A comparative study of matrix factorization and random walk with restart in recommender systems

Cited by 29 publications

References 26 publications

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Research on the Influence of Sampling Methods for the Accuracy of Web Services QoS Prediction

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

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