A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management (CIKM) 201

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

A novice-expert comparison in information search

In the age of Google, it is commonly believed that university students, especially those at postgraduate level, should have attained enough information searching skills to support their studies. However, recent researches have found that the information literacy level of quite a few postgraduate students is, in fact, far from satisfactory. One possible way for information search specialists to help students effectively search information is to use a novice-expert comparison to examine the differences between novices and experts in information search. The aim of this study is to uncover some of the major differences in the search query statements and information search strategies between eight doctoral students (novice searchers) and an expert information literacy professional. Preliminary findings show that conspicuous differences do exist in the complexity of the formulation of query statements, choice of keywords, use of operators between the novice and the expert searchers.postprin

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

Instrument development, data collection, and characteristics of practices, staff, and measures in the Improving Quality of Care in Diabetes (iQuaD) Study

Peer reviewedPublisher PD

Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce

In this paper, we present our work towards comparing on-line and off-line evaluation metrics in the context of small e-commerce recommender systems. Recommending on small e-commerce enterprises is rather challenging due to the lower volume of interactions and low user loyalty, rarely extending beyond a single session. On the other hand, we usually have to deal with lower volumes of objects, which are easier to discover by users through various browsing/searching GUIs. The main goal of this paper is to determine applicability of off-line evaluation metrics in learning true usability of recommender systems (evaluated on-line in A/B testing). In total 800 variants of recommending algorithms were evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based, novelty and diversity evaluation. The off-line results were afterwards compared with on-line evaluation of 12 selected recommender variants and based on the results, we tried to learn and utilize an off-line to on-line results prediction model. Off-line results shown a great variance in performance w.r.t. different metrics with the Pareto front covering 68\% of the approaches. Furthermore, we observed that on-line results are considerably affected by the novelty of users. On-line metrics correlates positively with ranking-based metrics (AUC, MRR, nDCG) for novice users, while too high values of diversity and novelty had a negative impact on the on-line results for them. For users with more visited items, however, the diversity became more important, while ranking-based metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc

Can NSEC5 be practical for DNSSEC deployments?

NSEC5 is proposed modification to DNSSEC that simultaneously guarantees two security properties: (1) privacy against offline zone enumeration, and (2) integrity of zone contents, even if an adversary compromises the authoritative nameserver responsible for responding to DNS queries for the zone. This paper redesigns NSEC5 to make it both practical and performant. Our NSEC5 redesign features a new fast verifiable random function (VRF) based on elliptic curve cryptography (ECC), along with a cryptographic proof of its security. This VRF is also of independent interest, as it is being standardized by the IETF and being used by several other projects. We show how to integrate NSEC5 using our ECC-based VRF into the DNSSEC protocol, leveraging precomputation to improve performance and DNS protocol-level optimizations to shorten responses. Next, we present the first full-fledged implementation of NSEC5—extending widely-used DNS software to present a nameserver and recursive resolver that support NSEC5—and evaluate their performance under aggressive DNS query loads. Our performance results indicate that our redesigned NSEC5 can be viable even for high-throughput scenarioshttps://eprint.iacr.org/2017/099.pdfFirst author draf