Temporal models for mining, ranking and recommendation in the Web

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dc.identifier.uri http://dx.doi.org/10.15488/9750
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/9806
dc.contributor.author Nguyen, Tu ger
dc.date.accessioned 2020-03-25T09:54:28Z
dc.date.available 2020-03-25T09:54:28Z
dc.date.issued 2020
dc.identifier.citation Nguyen, Tu: Temporal models for mining, ranking and recommendation in the Web. Hannover : Gottfried Wilhelm Leibniz Universität Hannover, Diss., 2020, xxii 142 S. DOI: https://doi.org/10.15488/9750 ger
dc.description.abstract Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time. ger
dc.language.iso eng ger
dc.publisher Hannover : Institutionelles Repositorium der Leibniz Universität Hannover
dc.rights CC BY 3.0 DE ger
dc.rights.uri http://creativecommons.org/licenses/by/3.0/de/ ger
dc.subject temporal dynamics eng
dc.subject ranking eng
dc.subject recommendation eng
dc.subject events eng
dc.subject zeitliche Dynamik ger
dc.subject Ranking ger
dc.subject Empfehlung ger
dc.subject Ereignisse ger
dc.subject.ddc 004 | Informatik ger
dc.title Temporal models for mining, ranking and recommendation in the Web ger
dc.type doctoralThesis ger
dc.type Text ger
dc.description.version publishedVersion ger
tib.accessRights frei zug�nglich ger


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