Inferring user intent in recommender systems can help performance but is difficult because intent is personal and not directly observable. Previous work has leveraged signals to stand as a proxy for intent (e.g. user interactions with resource pages), but such signals are not always available. In this paper, we instead recognize that certain events, which are observable, directly influence user intent. For example, after a flood, home improvement customers are more likely to undertake a renovation project to dry out their basement. We introduce MAWI Rec, a recommender system that leverages severe weather data to improve recommendation. Our weather-aware system achieves a significant improvement over a state-of-the-art baseline for online and in-store datasets of home improvement customers. This gain is most significant for weather-related product categories such as roof panels and flashings.

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.

Underground forums are widely used by criminals to buy and sell a host of stolen items, datasets, resources, and criminal services. These forums contain important resources for understanding cybercrime. However, the number of forums, their size, and the domain expertise required to understand the markets makes manual exploration of these forums unscalable. In this work, we propose an automated, top-down approach for analyzing underground forums. Our approach uses natural language processing and machine learning to automatically generate high-level information about underground forums, first identifying posts related to transactions, and then extracting products and prices. We also demonstrate, via a pair of case studies, how an analyst can use these automated approaches to investigate other categories of products and transactions. We use eight distinct forums to assess our tools: Antichat, Blackhat World, Carders, Darkode, Hack Forums, Hell, L33tCrew and Nulled. Our automated approach is fast and accurate, achieving over 80% accuracy in detecting post category, product, and prices.