Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba

Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as "what food is good for cold weather", because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using large-scale, real-world search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%.Comment: CIKM2019 International Workshop on Entity Retrieval. arXiv admin note: text overlap with arXiv:1511.08996 by other author

Animating the evolution of software

The use and development of open source software has increased significantly in the last decade. The high frequency of changes and releases across a distributed environment requires good project management tools in order to control the process adequately. However, even with these tools in place, the nature of the development and the fact that developers will often work on many other projects simultaneously, means that the developers are unlikely to have a clear picture of the current state of the project at any time. Furthermore, the poor documentation associated with many projects has a detrimental effect when encouraging new developers to contribute to the software. A typical version control repository contains a mine of information that is not always obvious and not easy to comprehend in its raw form. However, presenting this historical data in a suitable format by using software visualisation techniques allows the evolution of the software over a number of releases to be shown. This allows the changes that have been made to the software to be identified clearly, thus ensuring that the effect of those changes will also be emphasised. This then enables both managers and developers to gain a more detailed view of the current state of the project. The visualisation of evolving software introduces a number of new issues. This thesis investigates some of these issues in detail, and recommends a number of solutions in order to alleviate the problems that may otherwise arise. The solutions are then demonstrated in the definition of two new visualisations. These use historical data contained within version control repositories to show the evolution of the software at a number of levels of granularity. Additionally, animation is used as an integral part of both visualisations - not only to show the evolution by representing the progression of time, but also to highlight the changes that have occurred. Previously, the use of animation within software visualisation has been primarily restricted to small-scale, hand generated visualisations. However, this thesis shows the viability of using animation within software visualisation with automated visualisations on a large scale. In addition, evaluation of the visualisations has shown that they are suitable for showing the changes that have occurred in the software over a period of time, and subsequently how the software has evolved. These visualisations are therefore suitable for use by developers and managers involved with open source software. In addition, they also provide a basis for future research in evolutionary visualisations, software evolution and open source development

You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

Personalised online sales using web usage data mining

Practically every major company with a retail operation has its own web site and online sales facilities. This paper describes a toolset that exploits web usage data mining techniques to identify customer Internet browsing patterns. These patterns are then used to underpin a personalised product recommendation system for online sales. Within the architecture, a Kohonen neural network or self-organizing map (SOM) has been trained for use both offline, to discover user group profiles, and in real-time to examine active user click stream data, make a match to a specific user group, and recommend a unique set of product browsing options appropriate to an individual user. Our work demonstrates that this approach can overcome the scalability problem that is common among these types of system. Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations

Analytic provenance for sensemaking: A research agenda

Sensemaking is a process of finding meaning from information that often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved. This history, commonly known as provenance information, can be a resource for 'reflection-in-action'' during analysis, supporting collaboration between analysts, and can help trace data quality and uncertainty through the analysis process. Currently, there is limited work on utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. This article presents and extends the research challenges discussed in a IEEE VIS 2014 workshop on this topic to provide an agenda for sensemaking analytic provenance

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Exploring the motivation behind cybersecurity insider threat and proposed research agenda

Cyber exploitation and malicious activities have become more sophisticated. Insider threat is one of the most significant cyber security threat vector, while posing a great concern to corporations and governments. An overview of the fundamental motivating forces and motivation theory are discussed. Such overview is provided to identify motivations that lead trusted employees to become insider threats in the context of cyber security. A research agenda with two sequential experimental research studies are outlined to address the challenge of insider threat mitigation by a prototype development. The first proposed study will classify data intake feeds, as recognized and weighted by cyber security experts, in an effort to establish predictive analytics of novel correlations of activities that may lead to cyber security incidents. It will also develop approach to identify how user activities can be compared against an established baseline, the user’s network cyber security pulse, with visualization of simulated users’ activities. Additionally, the second study will explain the process of assessing the usability of a developed visualization prototype that intends to present correlated suspicious activities requiring immediate action. Successfully developing the proposed prototype via feeds aggregation and an advanced visualization from the proposed research could assist in the mitigation of malicious insider threat