Task-based user profiling for query refinement (toque)

The information needs of search engine users vary in complexity. Some simple needs can be satisfied by using a single query, while complicated ones require a series of queries spanning a period of time. A search task, consisting of a sequence of search queries serving the same information need, can be treated as an atomic unit for modeling user’s search preferences and has been applied in improving the accuracy of search results. However, existing studies on user search tasks mainly focus on applying user’s interests in re-ranking search results. Only few studies have examined the effects of utilizing search tasks to assist users in obtaining effective queries. Moreover, fewer existing studies have examined the dynamic characteristics of user’s search interests within a search task. Furthermore, even fewer studies have examined approaches to selective personalization for candidate refined queries that are expected to benefit from its application. This study proposes a framework of modeling user’s task-based dynamic search interests to address these issues and makes the following contributions. First, task identification: a cross-session based method is proposed to discover tasks by modeling the best-link structure of queries, based on the commonly shared clicked results. A graph-based representation method is introduced to improve the effectiveness of link prediction in a query sequence. Second, dynamic task-level search interest representation: a four-tuple user profiling model is introduced to represent long- and short-term user interests extracted from search tasks and sessions. It models user’s interests at the task level to re-rank candidate queries through modules of task identification and update. Third, selective personalization: a two-step personalization algorithm is proposed to improve the rankings of candidate queries for query refinement by assessing the task dependency via exploiting a latent task space. Experimental results show that the proposed TOQUE framework contributes to an increased precision of candidate queries and thus shortened search sessions

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

Personalized Image Search Using Complex Multiple Words Based Queries

No Abstrac

An Analysis of Using Expert Systems and Intelligent Agents for the Virtual Library Project at the Naval Surface Warfare Center-Carderock Division

The Virtual Library Project1 at the Naval Surface Warfare Center/Carderock Division (NSWC/CD) is being developed to facilitate the incorporation and use of library documents via the Internet. These documents typically relate to the design and manufacture of ships for the U.S. Navy Fleet. As such, the libraries will store documents that contain not only text but also images, graphs and design configurations. Because of the dynamic nature of digital documents, particularly those related to design, rapid and effective cataloging of these documents becomes challenging. We conducted a research study to analyze the use of expert systems and intelligent agents to support the function of cataloging digital documents. This chapter provides an overview of past research in the use of expert systems and intelligent agents for cataloging digital documents and discusses our recommendations based on NSWC/CD’s requirements

Overview of the CLEF 2018 personalised information retrieval lab (PIR-CLEF 2018)

At CLEF 2018, the Personalised Information Retrieval Lab (PIR-CLEF 2018) has been conceived to provide an initiative aimed at both providing and critically analysing a new approach to the evaluation of personalization in Information Retrieval (PIR). PIR-CLEF 2018 is the first edition of this Lab after the successful Pilot lab organised at CLEF 2017. PIR CLEF 2018 has provided registered participants with the data sets originally developed for the PIR-CLEF 2017 Pilot task; the data collected are related to real search sessions over a subset of the ClueWeb12 collection, undertaken by 10 users by using a novel methodology. The data were gathered during the search sessions undertaken by 10 volunteer searchers. Activities during these search sessions included relevance assessment of a retrieved documents by the searchers. 16 groups registered to participate at PIR-CLEF 2018 and were provided with the data set to allow them to work on PIR related tasks and to provide feedback about our proposed PIR evaluation methodology with the aim to create an effective evaluation task

Dynamic User Profiling for Search Personalisation

The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests. A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document. In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests. To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines

Enhancing Information Retrieval Relevance Using Touch Dynamics on Search Engine

Using Touch Dynamics on Search Engine is an attempt to establish the possibilities of using user touch behavior which is monitored and several unique features are extracted. The unique features are used for identifying users and their traits according to the touch dynamics. The results can be used for defining automatic user unique searching behavior. Touch dynamics has been discussed in several studies in the context of user authentication and biometric identification for security purposes. This study establishes the possibility of integrating touch dynamics results for identifying user searching preferences and interests. This study investigates a technique of combining personalized search with touch dynamics results information as an approach for determining user preferences, interest measurement and context. Keywords: Personalized Search, Information Retrieval, Touch Dynamics, Search Engin

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Recommending video content for use in group-based reminiscence therapy

REMPAD is a semi-automated cloud-based system used to facilitate digital reminiscence therapy for patients with mild-to-moderate dementia, enacted in a group setting. REMPAD uses profiles for participants and groups to proactively recommend interactive video content from the Internet to match these profiles. In this chapter, we focus on the design of the system and then the system architecture, the system build, data curation, and usage scenarios. We also report a series of steps carried out as part of our user-centered design approach to system development, and a series of analyses on interaction logs which indicate various levels of effectiveness for different configurations of the recommendation algorithm we use. The results indicate high user satisfaction when using the system, and strong tendency towards repeated use in future