Matrix Factorization Techniques for Recommender Systems

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.

/pdf/community-discovery-in-dynamic-networks-a-survey-5ao84ofaea.pdf

Community Discovery in Dynamic Networks: A Survey

Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data

Influence maximization is a classic optimization problem studied in the area of social network analysis and viral marketing. Given a network, it is defined as the problem of finding k seed nodes so that the influence spread of the network can be optimized. Kempe et al. have proved that this problem is NP hard and the objective function is submodular, based on which a greedy algorithm was proposed to give a near-optimal solution. However, this simple greedy algorithm is time consuming, which limits its application on large-scale networks. Heuristic algorithms generally cannot provide any performance guarantee. To solve this problem, in this paper we propose CoFIM, a community-based framework for influence maximization on large-scale networks. In our framework the influence propagation process is divided into two phases: (i) seeds expansion; and (ii) intra-community propagation. The first phase is the expansion of seed nodes among different communities at the beginning of diffusion. The second phase is the influence propagation within communities which are independent of each other. Based on the framework, we derive a simple evaluation form of the total influence spread which is submodular and can be efficiently computed. Then we further propose a fast algorithm to select the seed nodes. Experimental results on synthetic and nine real-world large datasets including networks with millions of nodes and hundreds of millions of edges show that our algorithm achieves competitive results in influence spread as compared with state-of-the-art algorithms and it is much more efficient in terms of both time and memory usage.

CoFIM: A community-based framework for influence maximization on large-scale networks

Recommendation can be reduced to a sub-problem of link prediction, with specific nodes (users and items) and links (similar relations among users/items, and interactions between users and items). However, previous link prediction approaches must be modified to suit recommendation instances because they neglect to distinguish the fundamental relations similar vs. dissimilar and like vs. dislike. Here, we propose a novel and unified way to cope with this deficiency, modeling the relational dualities using complex numbers. Previous works can still be used in this representation. In experiments with the MovieLens dataset and the Android software website AppChina.com, the proposed Complex Representation-based Link Prediction method (CORLP) achieves significant performance in accuracy and coverage compared with state-of-the-art methods. In addition, the results reveal several new findings. First, performance is improved, when the user and item degrees are taken into account. Second, the item degree plays a more important role than the user degree in the final recommendation. Given its notable performance, we are preparing to use the method in a commercial setting, AppChina.com, for application recommendation.

/pdf/a-link-prediction-approach-for-item-recommendation-with-dnlxq35voa.pdf

A link prediction approach for item recommendation with complex number

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.

A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

In human resource management, employee turnover problem is heavily concerned by managers since the leave of key employees can bring great loss to the company. However, most existing researches are employee-centered, which ignored the historical events of turnover behaviors or the longitudinal data of job records. In this paper, from an event-centered perspective, we design a hybrid model based on survival analysis and machine learning, and propose a turnover prediction algorithm named RFRSF, which combines survival analysis for censored data processing and ensemble learning for turnover behavior prediction. In addition, we take strategies to handle employees with multiple turnover records so as to construct survival data with censored records. We compare RFRSF with several baseline methods on a real dataset crawled from one of the biggest online professional social platforms of China. The results show that the survival analysis model can significantly benefit the employee turnover prediction performance.

RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis

Community detection is a fundamental task in network analysis. Applications on massive dynamic networks require more efficient solutions and lead to incremental community detection, which revises the community assignments of new or changed vertices during network updates. In this paper, we propose to use machine learning classifiers to predict the vertices that need to be inspected for community assignment revision. This learning-based targeted revision (LBTR) approach aims to improve community detection efficiency by filtering out the unchanged vertices from unnecessary processing. In this paper, we design features that can be used for efficient target classification and analyze the time complexity of our framework. We conduct experiments on two real-world datasets, which show our LBTR approach significantly reduces the computational time while keeping a high community detection quality. Furthermore, as compared with the benchmarks, we find our approach’s performance is stable on both growing networks and networks with vertex/edge removals. Experiments suggest that one should increase the target classification precision while keeping recall at a reasonable level when implementing our proposed approach. The study provides a unique perspective in incremental community detection.

Jiaxing Shang

Papers

CoFIM: A community-based framework for influence maximization on large-scale networks

A link prediction approach for item recommendation with complex number

A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis

Targeted revision: A learning-based approach for incremental community detection in dynamic networks