Personalized Search Via Neural Contextual Semantic Relevance Ranking

Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document content and user query context using both lexical representations and semantic embeddings such that the user's intent can be better understood by data enrichment of personalized query context information. Extensive experiments performed on the search dataset, demonstrate the effectiveness of the proposed method.Comment: Contextual, Personalization, Search, Semantics, LLM, embeddin

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Navigating Complex Search Tasks with AI Copilots

As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and charts a course toward new horizons in information access guided by AI copilots.Comment: 10 pages, 6 figure

Telelactation with a Mobile App: User Profile and Most Common Queries

Final publication is available from Mary Ann Liebert, Inc., publishers https://doi.org/10.1089/bfm.2020.0269Background: Mobile applications related to health issues are currently expanding. Different uses of new technologies have produced positive results regarding breastfeeding support. Breastfeeding applications are increasing. Objective: We conducted a descriptive analysis of a mobile application for breastfeeding (LactApp) to study the user profile and the most frequent queries. Materials and Methods: This was a retrospective, comparative, and descriptive ecological time-series study of LactApp from 2016 to 2019. Google Analytics and the app itself were used for data collection. The data were analyzed in Excel, and for the time series, Prais–Winsten autoregressions were applied based on the Durbin–Watson method in Stata. Results: A total of 115,830 users and 71,780 infants were registered in the application. A total of 1.91% of these users obtained the medical version. The application was used for both queries and surveys and for users to interact through chat. A total of 30.17% of the responses were related with “baby's sleep” (8.94%), 8.91% were related to “preservation of milk,” 6.16% were related to “breastfeeding crisis,” and 6.15% were related to “physiological evolution of breastfeeding,” all with an increasing trend. Conclusion: LactApp is a resource for breastfeeding that is widely downloaded and used by a substantial number of individuals. The most recurring topics were baby's sleep, milk extraction and preservation, breastfeeding crisis and physiological evolution of breastfeeding

Visual analytics for the interpretation of fluency tests during Alzheimer evaluation

International audienceA possible way to evaluate the progress of Alzheimer disease is to conduct the Isaac set test [13, 14]. In this activity, patients are asked to cite the largest possible number of city names within a minute. Since the city names are handwritten very quickly by a medical practitioner some cities are abbreviated or poorly written. In order to analyze such data, medical practitioners need to digitize the notes first and clean the dataset. Because these tasks are intricate and error prone we propose a novel set of tools, involving interactive visualization techniques, to help medical practitioners in the digitization and data-cleaning process. This system will be tested as part of an ongoing longitudinal study involving 9500 patients

Behavioral Task Modeling for Entity Recommendation

Our everyday tasks involve interactions with a wide range of information. The information that we manage is often associated with a task context. However, current computer systems do not organize information in this way, do not help the user find information in task context, but require explicit user actions such as searching and information seeking. We explore the use of task context to guide the delivery of information to the user proactively, that is, to have the right information easily available at the right time. In this thesis, we used two types of novel contextual information: 24/7 behavioral recordings and spoken conversations for task modeling. The task context is created by monitoring the user's information behavior from temporal, social, and topical aspects; that can be contextualized by several entities such as applications, documents, people, time, and various keywords determining the task. By tracking the association amongst the entities, we can infer the user's task context, predict future information access, and proactively retrieve relevant information for the task at hand. The approach is validated with a series of field studies, in which altogether 47 participants voluntarily installed a screen monitoring system on their laptops 24/7 to collect available digital activities, and their spoken conversations were recorded. Different aspects of the data were considered to train the models. In the evaluation, we treated information sourced from several applications, spoken conversations, and various aspects of the data as different kinds of influence on the prediction performance. The combined influences of multiple data sources and aspects were also considered in the models. Our findings revealed that task information could be found in a variety of applications and spoken conversations. In addition, we found that task context models that consider behavioral information captured from the computer screen and spoken conversations could yield a promising improvement in recommendation quality compared to the conventional modeling approach that considered only pre-determined interaction logs, such as query logs or Web browsing history. We also showed how a task context model could support the users' work performance, reducing their effort in searching by ranking and suggesting relevant information. Our results and findings have direct implications for information personalization and recommendation systems that leverage contextual information to predict and proactively present personalized information to the user to improve the interaction experience with the computer systems.Jokapäiväisiin tehtäviimme kuuluu vuorovaikutusta monenlaisten tietojen kanssa. Hallitsemamme tiedot liittyvät usein johonkin tehtäväkontekstiin. Nykyiset tietokonejärjestelmät eivät kuitenkaan järjestä tietoja tällä tavalla tai auta käyttäjää löytämään tietoja tehtäväkontekstista, vaan vaativat käyttäjältä eksplisiittisiä toimia, kuten tietojen hakua ja etsimistä. Tutkimme, kuinka tehtäväkontekstia voidaan käyttää ohjaamaan tietojen toimittamista käyttäjälle ennakoivasti, eli siten, että oikeat tiedot olisivat helposti saatavilla oikeaan aikaan. Tässä väitöskirjassa käytimme kahdenlaisia uusia kontekstuaalisia tietoja: 24/7-käyttäytymistallenteita ja tehtävän mallintamiseen liittyviä puhuttuja keskusteluja. Tehtäväkonteksti luodaan seuraamalla käyttäjän tietokäyttäytymistä ajallisista, sosiaalisista ja ajankohtaisista näkökulmista katsoen; sitä voidaan kuvata useilla entiteeteillä, kuten sovelluksilla, asiakirjoilla, henkilöillä, ajalla ja erilaisilla tehtävää määrittävillä avainsanoilla. Tarkastelemalla näiden entiteettien välisiä yhteyksiä voimme päätellä käyttäjän tehtäväkontekstin, ennustaa tulevaa tiedon käyttöä ja hakea ennakoivasti käsillä olevaan tehtävään liittyviä asiaankuuluvia tietoja. Tätä lähestymistapaa arvioitiin kenttätutkimuksilla, joissa yhteensä 47 osallistujaa asensi vapaaehtoisesti kannettaviin tietokoneisiinsa näytönvalvontajärjestelmän, jolla voitiin 24/7 kerätä heidän saatavilla oleva digitaalinen toimintansa, ja joissa tallennettiin myös heidän puhutut keskustelunsa. Mallien kouluttamisessa otettiin huomioon datan eri piirteet. Arvioinnissa käsittelimme useista sovelluksista, puhutuista keskusteluista ja datan eri piirteistä saatuja tietoja erilaisina vaikutuksina ennusteiden toimivuuteen. Malleissa otettiin huomioon myös useiden tietolähteiden ja näkökohtien yhteisvaikutukset. Havaintomme paljastivat, että tehtävätietoja löytyi useista sovelluksista ja puhutuista keskusteluista. Lisäksi havaitsimme, että tehtäväkontekstimallit, joissa otetaan huomioon tietokoneen näytöltä ja puhutuista keskusteluista saadut käyttäytymistiedot, voivat parantaa suositusten laatua verrattuna tavanomaiseen mallinnustapaan, jossa tarkastellaan vain ennalta määritettyjä vuorovaikutuslokeja, kuten kyselylokeja tai verkonselaushistoriaa. Osoitimme myös, miten tehtäväkontekstimalli pystyi tukemaan käyttäjien suoritusta ja vähentämään heidän hakuihin tarvitsemaansa työpanosta järjestämällä hakutuloksia ja ehdottamalla heille asiaankuuluvia tietoja. Tuloksillamme ja havainnoillamme on suoria vaikutuksia tietojen personointi- ja suositusjärjestelmiin, jotka hyödyntävät kontekstuaalista tietoa ennustaakseen ja esittääkseen ennakoivasti personoituja tietoja käyttäjälle ja näin parantaakseen vuorovaikutuskokemusta tietokonejärjestelmien kanssa

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