SITUACIÓN HUMANA

dependence on information systems to support our everyday activities. At the same time, past temporal dynamics provides a dimension to structure the meaning of information, beyond the conventional characterisation of information afforded by stationary statistical distributions which assume information and information behaviour remain static over time. In long-term time-based information collections, distinguishing the temporal dimension is likely to be- come increasingly important in characterising the meaning of information. Consequently, supporting and exploiting temporal dynamics can be viewed complementarily as ‘two sides of the same coin’. That is, supporting real-time temporal dynamics requires exploiting past temporal dynamics, and vice-versa for consistent effectiveness over time.

Overall, in this thesis I have presented and evaluated several techniques which (i) support the real-time temporal dynamics of information seeking to maintain consistent user satisfac- tion, and (ii) exploit past temporal dynamics as a tacit dimension to inform more effective IR systems. Connected strands of time are diverse in IR, but temporal dynamics are a com- mon thread in many streams of individual and social information and information behaviour. Bringing these strands together with a unified understanding of why users develop informa- tion needs, what motivates their need and thus their expectations, and the temporal signals defining the meaning of relevant information over time will lead to considerable future im- pact by providing more consistently effective IR systems. While a unified model of time in IR is some way off, together the novel contributions both reinforce and establish several novel avenues for incorporating temporal dynamics in time-aware IR, and therefore provide a strong foundation to support information behaviour in present and future IR systems.

7.4

Future Directions

This thesis has explored many challenges are opportunities of temporal dynamics in IR. Sev- eral avenues have emerged for future work. In the conclusions provided at the end of each research contribution chapter (i.e., Chapters 4 to 6, inclusive), I included future work specific to the approaches and techniques explored in the respective chapter. In the following sections I detail more general opportunities relating to temporal dynamics in time-aware IR.

Understanding the Impact of Time-Evolving Collections on Retrieval Models

In this thesis I have shown that index term frequency, specificity and relationships all ex- hibit temporal dynamics in time-based collections which evolve over time. Despite the ma- jority of collections evolving over time, there has been little work to study the impact of changing collections on retrieval model performance. The standard systems-based evaluation methodology (i.e., much of TREC, with the exception of TREC Microblogging), assumes a

7.4 Future Directions static collection. Consequently, many retrieval models and techniques (e.g., query expansion) have been developed and tested using static collection snapshots. Given that the fundamental statistical distributions upon which many of these approaches are based change over time, their transferability to real scenarios where temporal change is ever present needs more in- vestigation. Indeed, retrieval effectiveness is likely subject to temporal dynamics. Further work should examine how retrieval performance varies as the collections grows, and new and evolving topics impact retrieval model effectiveness for previously satisfactory results.

Enhanced Time Series Modelling for Event-aware IR

Time-aware IR is often reliant on predicting the occurrence and impact of future events and phenomena. Accordingly, time series models have been used extensively to predict future time-based activity, based on modelling short- and long-term trends, periodicity and surprises in previously observed activity (Radinsky et al., 2013b). In particular, in IR times series models have been employed to predict future query popularity (Shokouhi, 2013), result clicks (Radinsky et al., 2013b) and index term weighting (Efron, 2010).

Conventional time series models such as auto-regressive moving averages (e.g., ARMA and ARIMA techniques) and exponential smoothing are based purely on past time series obser- vations. Hence, they do not take external factors outwith the previously observed time series into consideration. However, structured and unstructured external factors, such as topics of discussion in a social network, and their authors influence, could prove very predictive of a given time series such as the popularity of a related query. For example, discussion about a particular actor in an upcoming highly anticipated television show may be highly predictive of upcoming query popularity for that actor, and their related entities.

Recent advances in machine learning techniques, in particular deep artificial neural networks, facilitate representing and learning vast and deep feature spaces for time series modelling (Boulanger-Lewandowski, 2014). Indeed, these new large-scale knowledge representation techniques may open up many opportunities for powerful time series modelling based on rich sets of previously observed time series trends and patterns, in addition to external fac- tor features (e.g., high dimensional term occurrence during information cascades in a so- cial network). The potential prediction accuracy afforded by these techniques is likely to have considerable impact on time-aware IR techniques which can better anticipate upcoming change. With more reliable knowledge of the future, there will be an opportunity to pursue

event-awareIR which pro-actively responds to upcoming changes expected to affect user ex-

pectations. This is in contrast to current time-aware IR approaches which are typically more reactive as they evolve based on changes seen in previous observations. Indeed, event-aware

7.5 Chapter Overview