Living analytics methods for the social web

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Large-scale Dynamic Network Representation via Tensor Ring Decomposition

Large-scale Dynamic Networks (LDNs) are becoming increasingly important in the Internet age, yet the dynamic nature of these networks captures the evolution of the network structure and how edge weights change over time, posing unique challenges for data analysis and modeling. A Latent Factorization of Tensors (LFT) model facilitates efficient representation learning for a LDN. But the existing LFT models are almost based on Canonical Polyadic Factorization (CPF). Therefore, this work proposes a model based on Tensor Ring (TR) decomposition for efficient representation learning for a LDN. Specifically, we incorporate the principle of single latent factor-dependent, non-negative, and multiplicative update (SLF-NMU) into the TR decomposition model, and analyze the particular bias form of TR decomposition. Experimental studies on two real LDNs demonstrate that the propose method achieves higher accuracy than existing models

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset

A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning the limited interactions among numerous nodes. Knowledge acquisition from HDI data is a vital issue in the domain of data science due to their embedded rich patterns like node behaviors, where the fundamental task is to perform HDI data representation learning. Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence. However, existing LBI schemes are all statistic ones where the linear biases are fixed, which significantly restricts the scalability of the resultant NLFA model and results in loss of representation learning ability to HDI data. Motivated by the above discoveries, this paper innovatively presents the dynamic linear bias incorporation (DLBI) scheme. It firstly extends the linear bias vectors into matrices, and then builds a binary weight matrix to switch the active/inactive states of the linear biases. The weight matrix's each entry switches between the binary states dynamically corresponding to the linear bias value variation, thereby establishing the dynamic linear biases for an NLFA model. Empirical studies on three HDI datasets from real applications demonstrate that the proposed DLBI-based NLFA model obtains higher representation accuracy several than state-of-the-art models do, as well as highly-competitive computational efficiency.Comment: arXiv admin note: substantial text overlap with arXiv:2306.03911, arXiv:2302.12122, arXiv:2306.0364