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Greenstein-Messica, Asnat, Rokach, Lior, Friedman, Michael.  2017.  Session-Based Recommendations Using Item Embedding. Proceedings of the 22Nd International Conference on Intelligent User Interfaces. :629–633.

Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.

Grushka - Cohen, Hagit, Sofer, Oded, Biller, Ofer, Shapira, Bracha, Rokach, Lior.  2016.  CyberRank: Knowledge Elicitation for Risk Assessment of Database Security. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. :2009–2012.
Security systems for databases produce numerous alerts about anomalous activities and policy rule violations. Prioritizing these alerts will help security personnel focus their efforts on the most urgent alerts. Currently, this is done manually by security experts that rank the alerts or define static risk scoring rules. Existing solutions are expensive, consume valuable expert time, and do not dynamically adapt to changes in policy. Adopting a learning approach for ranking alerts is complex due to the efforts required by security experts to initially train such a model. The more features used, the more accurate the model is likely to be, but this will require the collection of a greater amount of user feedback and prolong the calibration process. In this paper, we propose CyberRank, a novel algorithm for automatic preference elicitation that is effective for situations with limited experts' time and outperforms other algorithms for initial training of the system. We generate synthetic examples and annotate them using a model produced by Analytic Hierarchical Processing (AHP) to bootstrap a preference learning algorithm. We evaluate different approaches with a new dataset of expert ranked pairs of database transactions, in terms of their risk to the organization. We evaluated using manual risk assessments of transaction pairs, CyberRank outperforms all other methods for cold start scenario with error reduction of 20%.
Mirsky, Yisroel, Shabtai, Asaf, Rokach, Lior, Shapira, Bracha, Elovici, Yuval.  2016.  SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research. Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. :1–12.

In this paper we describe and share with the research community, a significant smartphone dataset obtained from an ongoing long-term data collection experiment. The dataset currently contains 10 billion data records from 30 users collected over a period of 1.6 years and an additional 20 users for 6 months (totaling 50 active users currently participating in the experiment). The experiment involves two smartphone agents: SherLock and Moriarty. SherLock collects a wide variety of software and sensor data at a high sample rate. Moriarty perpetrates various attacks on the user and logs its activities, thus providing labels for the SherLock dataset. The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. To demonstrate possible uses of the dataset, we perform a basic malware analysis and evaluate a method of continuous user authentication.