Study on Methods and Estimation of Location Aware Keyword Query Suggestion

We plan a location-aware keyword query suggestion. We propose a weighted watchword record diagram, which catches both the semantic pertinence between catchphrase inquiries and the spatial separation between the subsequent archives and the client area. The diagram is perused in an irregular stroll with-restart form, to choose the catchphrase inquiries with the most elevated scores as recommendations. To make our structure adaptable, we propose a segment based methodology that outflanks the pattern algorithm by up to a request of size. The suitability of our system and the execution of the algorithms are assessed utilizing genuine informatio

查询推荐研究综述

查询推荐是一种提高用户搜索效率的重要技术，其核心任务是帮助用户构造有效查询并以此准确描述用户信息需求。作为当今搜索引擎的核心技术之一，查询推荐吸引了学术界和工业界的广泛关注，一直以来都是信息检索领域中重要的研究主题。本文以国内外会议、期刊发表的有关查询推荐研究的文献为对象，利用归纳总结方法，首先详细梳理了查询推荐中主流方法&mdash;&mdash;基于简单共现信息的方法、基于图模型的方法以及融合多种信息的方法，然后总结评述了评测方法与指标，最后分析了未来可能的研究方向。</p

Analyzing intentions from big data traces of human activities

The rapid growth of big data formed by human activities makes research on intention analysis both challenging and rewarding. We study multifaceted problems in analyzing intentions from big data traces of human activities, and such problems span a range of machine learning, optimization, and security and privacy. We show that analyzing intentions from industry-scale human activity big data can effectively improve the accuracy of computational models. Specifically, we take query auto-completion as a case study. We identify two hitherto-undiscovered problems: adaptive query auto-completion and mobile query auto-completion. We develop two computational models by analyzing intentions from big data traces of human activities on search interface interactions and on mobile application usage respectively. Solving the large-scale optimization problems in the proposed query auto-completion models drives deeper studies of the solvers. Hence, we consider the generalized machine learning problem settings and focus on developing lightweight stochastic algorithms as solvers to the large-scale convex optimization problems with theoretical guarantees. For optimizing strongly convex objectives, we design an accelerated stochastic block coordinate descent method with optimal sampling; for optimizing non-strongly convex objectives, we design a stochastic variance reduced alternating direction method of multipliers with the doubling-trick. Inevitably, human activities are human-centric, thus its research can inform security and privacy. On one hand, intention analysis research from human activities can be motivated from the security perspective. For instance, to reduce false alarms of medical service providers' suspicious accesses to electronic health records, we discover potential de facto diagnosis specialties that reflect such providers' genuine and permissible intentions of accessing records with certain diagnoses. On the other hand, we examine the privacy risk in anonymized heterogeneous information networks representing large-scale human activities, such as in social networking. Such data are released for external researchers to improve the prediction accuracy for users' online social networking intentions on the publishers' microblogging site. We show a negative result that makes a compelling argument: privacy must be a central goal for sensitive human activity data publishers