Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.

Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC) and cost-per-action (CPA). An accurate prediction of the probability that users click on ads is a crucial task in NGD advertising because this value is required to compute the expected revenue. State-of-the-art prediction algorithms rely heavily on historical information collected for advertisers, users and publishers. Click prediction of new ads in the system is a challenging task due to the lack of such historical data. The objective of this paper is to mitigate this problem by integrating multimedia features extracted from display ads into the click prediction models. Multimedia features can help us capture the attractiveness of the ads with similar contents or aesthetics. In this paper we evaluate the use of numerous multimedia features (in addition to commonly used user, advertiser and publisher features) for the purposes of improving click prediction in ads with no history. We provide analytical results generated over billions of samples and demonstrate that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.

In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the conversion rate (CVR). CVRs measure the proportion of certain users who take a predefined, desirable action, such as a purchase, registration, download, etc.; as compared to simply page browsing. We provide a detailed analysis of conversion rates in the context of non-guaranteed delivery targeted advertising. In particular we focus on the post-click conversion (PCC) problem or the analysis of conversions after a user click on a referring ad. The key elements we study are the probability of a conversion given a click in a user/page context, P(conversion | click, context). We provide various fundamental properties of this process based on contextual information, formalize the problem of predicting PCC, and propose an approach for measuring attribute relevance when the underlying attribute distribution is non-stationary. We provide experimental analyses based on logged events from a large-scale advertising platform.

Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers' return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.

This paper presents a framework called localized support vector machine (LSVM) for classifying data with nonlinear decision surfaces. Instead of building a sophisticated global model from the training data, LSVM constructs multiple linear SVMs, each of which is designed to accurately classify a given test example. A major limitation of this framework is its high computational cost since a unique model must be constructed for each test example. To overcome this limitation, we propose an efficient implementation of LSVM, termed profile SVM (PSVM). PSVM partitions the training examples into clusters and builds a separate linear SVM model for each cluster. Our empirical results show that (1) LSVM and PSVM outperform nonlinear SVM for all 20 of the evaluated data sets and (2) PSVM achieves comparable performance as LSVM in terms of model accuracy but with significant computational savings. We also demonstrate the efficacy of the proposed approaches in terms of classifying data with spatial and temporal dependencies.

Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other domains. This paper presents a robust algorithm for detecting anomalies in noisy multivariate time series data by employing a kernel matrix alignment method to capture the dependence relationships among variables in the time series. Anomalies are found by performing a random walk traversal on the graph induced by the aligned kernel matrix. We show that the algorithm is flexible enough to handle different types of time series anomalies including subsequence-based and local anomalies. Our framework can also be used to characterize the anomalies found in a target time series in terms of the anomalies present in other time series. We have performed extensive experiments to empirically demonstrate the effectiveness of our algorithm. A case study is also presented to illustrate the ability of the algorithm to detect ecosystem disturbances in Earth science data.

Remote sensing data from Earth observation satellites offer unprecedented opportunity for predicting and understanding the behavior of the Earth’s ecosystem. However, because of their massive volume, extracting interesting patterns such as ecosystem disturbances from the data is a challenging task. In this paper, we present a case study on the application of data mining to the disturbance event detection problem. We describe two approaches—moving average and random walk—for detecting ecosystem disturbances. We then illustrate how clustering can be used to identify locations with similar incidents of ecosystem disturbance events. Finally, we develop a clustering-based framework to aid the visual exploration and detection of ecosystem disturbances from high resolution vegetation cover data.

This paper presents a data oriented approach to modeling the complex computing systems, in which an ensemble of correlation models are discovered to represent the system status. If the discovered correlations can continually hold under different user scenarios and workloads, they are regarded as invariants of the information system. In our previous work, we have developed an algorithm to automatically search the invariants between any pair of system attributes, which we call local invariants. However that method is unable to deal with the high order dependency models due to the combinatorial explosion of search space. In this paper we use Bayesian regression technique to discover those high order correlation models, called global invariants. We treat each attribute as a response variable in turn and express its dependency with the other attributes in a regression model. By adding the prior constraint of Laplacian distribution to the regression coefficients, we can find the solution in which only the correlated attributes with respect to the response have nonzero regression coefficients. After that we further consider the temporal dependencies of those extracted attributes by incorporating their past observations. We also provide a confidence metric and a validation procedure to measure the reliability of learned models. If the model does not break down in the validation, it is regarded as a true invariant of the system. Experimental results on a real wireless networking system show that the discovered invariants can be used to effectively detect system failures as well as provide valuable information about the failure source.

Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one prediction step to the next step. Yet, there is a broad range of applications such as climate impact assessments and urban growth planning that require long term forecasting capabilities for strategic decision making. Training an accurate model that produces reliable long term predictions would require an extensive amount of historical data, which are either unavailable or expensive to acquire. For some of these domains, there are alternative ways to generate potential scenarios for the future using computer-driven simulation models, such as global climate and traffic demand models. However, the data generated by these models are currently utilized in a supervised learning setting, where a predictive model trained on past observations is used to estimate the future values. In this paper, we present a semi-supervised learning framework for long-term time series forecasting based on Hidden Markov Model Regression. A covariance alignment method is also developed to deal with the issue of inconsistencies between historical and model simulation data. We evaluated our approach on data sets from a variety of domains, including climate modeling. Our experimental results demonstrate the efficacy of the approach compared to other supervised learning methods for long-term time series forecasting.

This paper presents an algorithm for recommending items using a diverse set of features. The items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. To support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the massive size of the graph. To overcome this problem, we apply multi-way clustering to group together the highly correlated nodes. A recommendation is then made by traversing the subgraph induced by clusters associated with a user's interest. Our experimental results on real data sets demonstrate the efficacy of the proposed algorithm.

Nonlinear Support Vector Machines employ sophisticated kernel functions to classify data sets with complex decision surfaces. Determining the right parameters of such functions is not only computationally expensive, the resulting models are also susceptible to overfitting due to their large VC dimensions. Instead of fitting a nonlinear model, this paper presents a framework called Localized Support Vector Machine (LSVM), which builds multiple linear SVM models from the training data. Since each model is designed to classify a particular test example, it has high computational cost. To overcome this limitation, we propose an efficient implementation of LSVM, termed Profile SVM (PSVM). PSVM partitions the training examples into clusters and builds a separate linear SVM model for each cluster. Our empirical results show that (1) Both LSVM and PSVM outperform nonlinear SVM on the majority of the evaluated data sets; and (2) PSVM achieves comparable accuracy as LSVM but with significant computational savings.

This paper presents a principled approach for incorporating labeled examples into an anomaly detection task. We demonstrate that, with the addition of labeled examples, the anomaly detection algorithm can be guided to develop better models of the normal and abnormal behavior of the data, thus improving the detection rate and reducing the false alarm rate of the algorithm. A framework based on the finite mixture model is introduced to model the data as well as the constraints imposed by the labeled examples. Empirical studies conducted on real data sets show that significant improvements in detection rate and false alarm rate are achieved using our proposed framework.

Incorporating background knowledge into unsupervised clustering algorithms has been the subject of extensive research in recent years. Nevertheless, existing algorithms implicitly assume that the background information, typically specified in the form of labeled examples or pairwise constraints, has the same feature space as the unlabeled data to be clustered. In this paper, we are concerned with a new problem of incorporating partial background knowledge into clustering, where the labeled examples have moderate overlapping features with the unlabeled data. We formulate this as a constrained optimization problem, and propose two learning algorithms to solve the problem, based on hard and fuzzy clustering methods. An empirical study performed on a variety of real data sets shows that our proposed algorithms improve the quality of clustering results with limited labeled examples.