Five new papers are out 🙂
1. A joint work with my friend and colleague Prof. Tamir Tassa: “Mediated Secure Multi-Party Protocols for Collaborative Filtering”, was published in ACM Transactions on Intelligent Systems and Technology.
Recommender systems have become extremely common in recent years, and are utilized in a variety of domains such as movies, music, news, products, restaurants, etc. While a typical recommender system bases its recommendations solely on users’ preference data collected by the system itself, the quality of recommendations can significantly be improved if several recommender systems (or vendors) share their data. However, such data sharing poses significant privacy and security challenges, both to the vendors and the users. In this paper we propose secure protocols for distributed item-based Collaborative Filtering. Our protocols allow to compute both the predicted ratings of items and their predicted rankings, without compromising privacy nor predictions’ accuracy. Unlike previous solutions in which the secure protocols are executed solely by the vendors, our protocols assume the existence of a mediator that performs intermediate computations on encrypted data supplied by the vendors. Such a mediated setting is advantageous over the non-mediated one since it enables each vendor to communicate solely with the mediator. This yields reduced communication costs and it allows each vendor to issue recommendations to its clients without being dependent on the availability and willingness of the other vendors to collaborate.
2. A joint work with my Ph.D. student Dana Pessach and my colleagues Gonen Singer, Dan Avrahami, Hila Chalutz Ben-Gal and Irad Ben-Gal: “Employees Recruitment: A Prescriptive Analytics Approach via Machine Learning and Mathematical Programming”, was published in Decision Support Systems.
In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments’ success at the level of a single job placement and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning model, which in this case is obtained by applying the variable-order Bayesian network (VOBN) model to recruitment data. Specifically, we used a uniquely large dataset that contains the recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN can provide both high accuracy and interpretability insights to HR personnel. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a new employee in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.
3. A joint work with my M.Sc. student Tomer Yanay: “Air-Writing Recognition using Smart-bands”, was published in Pervasive and Mobile Computing.
We propose a novel approach for textual input which is based on air-writing recognition using smart-bands. The proposed approach enables the user to hand-write in the air in an intuitive and natural way, where text is recognized by analyzing the motion signals captured by an off-the-shelf smart-band worn by the user. Unlike existing studies that proposed the use of motion signals to recognize written letters, our approach does not require an extra dedicated device, nor it imposes unnecessary limitations on the writing process of the user. To test the feasibility of the new approach, we developed two air-writing recognition methods: a user-dependent method, based on K-Nearest-Neighbors with Dynamic-Time-Warping as the distance measure, and a user-independent method, based on a Convolutional-Neural-Network. The first creates a tailored model for each user, using a set of reference samples collected from the user in an enrollment phase, and therefore has the potential to be more accurate. The latter involves a preliminary training phase which generates a single model to all users, and therefore does not require an enrollment phase for new users. In order to evaluate our methods, we collected 15 sets of the English alpha-bet letters (written on the air and collected using a smart-band) from 55 different subjects. The results of our evaluation demonstrate the ability of the proposed methods to successfully recognize air-written letters with a high degree of accuracy, obtaining 89.2% average accuracy for the user-dependent method, and 83.2% average accuracy (95.6% when applying an auto-correction phase) for the user-independent method.
4. A joint work with my colleagues P.M. Krafft, T.L. Griffiths, J.B. Tenenbaum, A.S. Pentland: “Bayesian Collective Learning Emerges from Heuristic Social Learning”, was published in Cognition.
Many researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning – the use of information about other people’s decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a population can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform.
5. A joint work with my colleague Dr. Lihi Dery: “Improving Label Ranking Ensembles using Boosting Techniques”, was published in IEEE Access.
Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. Herein, we present a novel boosting algorithm, BoostLR, that was specifically designed for label ranking tasks. Similarly to other boosting algorithms, BoostLR, proceeds in rounds, where in each round, a single weak model is trained over a sampled set of instances. Instances that were identified as harder to predict in the current round, receive a higher (boosted) weight, and therefore also a higher probability to be included in the sample of the forthcoming round. Extensive evaluation of our proposed algorithm on 24 semi-synthetic and real-world label ranking datasets concludes that our algorithm significantly outperforms the current state-of-the-art label ranking methods.