Context-Aware Systems for Sequential Item Recommendation

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space

On trust and privacy in context-aware systems

Recent advances in networking, handheld computing and sensors technologies have led to the emergence of context-aware systems. The vast amounts of personal information collected by such systems has led to growing concerns about the privacy of their users. Users concerned about their private information are likely to refuse participation in such systems. Therefore, it is quite clear that for any context-aware system to be acceptable by the users, mechanisms for controlling access to personal information are a necessity. According to Alan Westin "privacy is the claim of individuals, groups, or institutions to determine for themselves when, how and to what extent information is communicated to others"1. Within this context we can classify users as either information owners or information receivers. It is also acknowledged that information owners are willing to disclose personal information if this disclosure is potentially beneficial. So, the acceptance of any context-aware system depends on the provision of mechanisms for fine-grained control of the disclosure of personal information incorporating an explicit notion of benefit

Trust-based model for privacy control in context aware systems

In context-aware systems, there is a high demand on providing privacy solutions to users when they are interacting and exchanging personal information. Privacy in this context encompasses reasoning about trust and risk involved in interactions between users. Trust, therefore, controls the amount of information that can be revealed, and risk analysis allows us to evaluate the expected benefit that would motivate users to participate in these interactions. In this paper, we propose a trust-based model for privacy control in context-aware systems based on incorporating trust and risk. Through this approach, it is clear how to reason about trust and risk in designing and implementing context-aware systems that provide mechanisms to protect users' privacy. Our approach also includes experiential learning mechanisms from past observations in reaching better decisions in future interactions. The outlined model in this paper serves as an attempt to solve the concerns of privacy control in context-aware systems. To validate this model, we are currently applying it on a context-aware system that tracks users' location. We hope to report on the performance evaluation and the experience of implementation in the near future

Context-aware systems architecture (CaSA)

Context-aware systems are becoming increasingly mainstream as more and more technology allows real-time collection of daily life data and it is more and more affordable to provide useful services to citizens in various situations of need. However, developers in this field are not well supported. Naturally we have inherited a number of methods and tools from past software engineering efforts to create previous computing systems. However the most recent generation of systems dominated by sensing supported context-awareness integrating a variety of data sources and with a higher expectation of personalized services delivered at the right time, place and in the right form, are not well supported. Developers need more guidance and support to pinpoint those valuable contexts and to work out ways of detecting them and activating the right services associated with these contexts. Our community has reported on various systems they created however not much is emerging in a way of a methodology, a standard, a transferable body of advice and guidance which can help teams next time they need to develop a new system. In this article we explain a couple of complementary methodologies which we have tried and tested through development of different context-aware projects. We argue these are of practical usefulness and provide an initial valid point of discussion for our community to create evolved versions of these which can be tested more widely to identify good practice in the area

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

Study on distributed context-aware systems

制度:新 ; 文部省報告番号:甲2059号 ; 学位の種類:博士(情報科学) ; 授与年月日:2005/3/15 ; 早大学位記番号:新401