Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.

/pdf/a-survey-on-learning-to-hash-42pkeg5nzu.pdf

A Survey on Learning to Hash

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation . It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.

Heterogeneous Information Network Embedding for Recommendation

https://link.springer.com/content/pdf/10.1057/jors.1979.126.pdf

Forecasting Economic Time Series

In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a social collaborative filtering algorithm using heterogeneous relations Distinct from the exiting methods, Hete-CF can effectively utilise multiple types of relations in a heterogeneous social network More importantly, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information The experimental results on a real-world dataset DBLP (a typical heterogeneous information network)demonstrate the effectiveness of our algorithm

/pdf/hete-cf-social-based-collaborative-filtering-recommendation-h7smdngzkg.pdf

Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations

As online services have more and more popular, incident diagnosis has emerged as a critical task in minimizing the service downtime and ensuring high quality of the services provided For most online services, incident diagnosis is mainly conducted by analyzing a large amount of telemetry data collected from the services at runtime Time series data and event sequence data are two major types of telemetry data Techniques of correlation analysis are important tools that are widely used by engineers for data-driven incident diagnosis Despite their importance, there has been little previous work addressing the correlation between two types of heterogeneous data for incident diagnosis: continuous time series data and temporal event data In this paper, we propose an approach to evaluate the correlation between time series data and event data Our approach is capable of discovering three important aspects of event-timeseries correlation in the context of incident diagnosis: existence of correlation, temporal order, and monotonic effect Our experimental results on simulation data sets and two real data sets demonstrate the effectiveness of the algorithm

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/SIGKDD-2014-Correlating-Events-with-Time-Series-for-Incident-Diagnosis.pdf

Correlating events with time series for incident diagnosis

Can public social media data be harnessed to predict COVID-19 case counts? We analyzed approximately 15 million COVID-19 related posts on Weibo, a popular Twitter-like social media platform in China, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. We then modeled the predictive power of sick posts and other COVID-19 posts on daily case counts. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics. But other COVID-19 posts did not have similar predictive power. For a subset of geotagged posts (3.10% of all retrieved posts), we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, it is crucial to sift through the contents and efficiently identify true signals from noise.

/pdf/using-reports-of-own-and-others-symptoms-and-diagnosis-on-3rgzlo6qxt.pdf

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm.

Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Transductive classification TC using a small labeled data to help classifying all the unlabeled data in information networks. It is an important data mining task on information networks. Various classification methods have been proposed for this task. However, most of these methods are proposed for homogeneous networks but not for heterogeneous ones, which include multi-typed objects and relations and may contain more useful semantic information. In this paper, we firstly use the concept of meta path to represent the different relation paths in heterogeneous networks and propose a novel meta path selection model. Then we extend the transductive classification problem to heterogeneous information networks and propose a novel algorithm, named HetPathMine. The experimental results show that: 1 HetPathMine can get higher accuracy than the existing transductive classification methods and 2 the weight obtained by HetPathMine for each meta path is consistent with human intuition or real-world situations.

Chen Luo

Papers

Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations

Correlating events with time series for incident diagnosis

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China

Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks