towards context-aware task recommendation c.c. vo, t. torabi, and s. w. loke la trobe university 1...

Towards Context-Aware Task Recommendation

C.C. Vo, T. Torabi, and S. W. LokeLa Trobe University

1

Presenter: Seng W. Loke

Contents

• Introduction• Proposed solution• Methodology• System architecture• Implementation• Related work• Conclusion and future work

2C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

Introduction

• “…technologies weave themselves into the fabric of everyday life…” [Weiser 1991].

• Challenge– support our activities, complement our skills, add to

our pleasure, convenience, accomplishments, but not to our stress [Norman 2007].

• Three problems from contradictions– Richness of features vs. Complexity of use,– Everywhere technologies vs. Invisibility of features,– Brand identification, product differentiation,

multimodal user interfaces (UIs) vs. Inconsistency of UIs3C.C. Vo, T. Torabi, and S. W. Loke - La Trobe

University

Introduction (cont.)• Complexity– Users are overwhelmed by overload of services,

features, and configurations [Garlan et al. 2002].– Complexity exceeds capacity of UI designs for users to

operate them intuitively [Rich 2009]• Invisibility– Originated from the perfectly and naturally

integrating technologies into environments [Pinto 2008].

– New places or even familiar places with frequently added/removed devices.

• Inconsistency [Rich 2009; Oliveira 2008]– (As mentioned in the previous slide)


Proposed solution

• Context-Aware Task Recommendation– Recommend relevant tasks based on user’s current

context (main content of this paper)– Guide user to accomplish selected tasks subject to

available capabilities of environment.• Task-Based UIs– UIs for interaction between user & environment are

tailored to tasks at hand.– Task-based UIs focus on “what to do” rather than

“how to do” [Wang et al. 2000; Masuoka et al. 2003].We aim to deal with the question:“What TASKS should I do with DEVICES, INFORMATION, and SERVICES I have?”


Concepts

• Task– “a set of actions performed collaboratively by humans

and machines to achieve a goal”.– Ex., “Make room warm”, and “Make tea”.

• Context– “any information that can be used to characterize the

situation of an entity” [Dey 2001].• Situation– Characterized by contextual information– A situation s is a vector of contexts: s = (c1,c2,…,cn)– (Time = ‘Monday’, Place = ‘Library’) is a situation.


Methodology

• From the behavioral psychology science:“People behave similarly in similar situations.” [Magnusson

et al. 1978]

our assumption“People do similar tasks in similar situations.”

• Our strategies for task recommendation– Situation similarity based collaborative filtering,– Knowledge-based filtering, and – Utility-based filtering.


Methodology (cont. 1)• Situation similarity based collaborative filtering

– Similarity between two tasks• Identified based on their effects on the situation• Ex., “Open windows” and “Turn on overhead lights” are similar as they are

both for increasing the brightness.– Similarity between two situations

• Pure similarity– Based on the similarity of local values of context attributes.

• Task-based situation similarity– Two situations are similar if the tasks typically accomplished in these situations are

similar.– Ex., “In-Meeting” and “In-Theatre” are similar with respect to the task of “Change

mobile to quiet mode” because people usually switch their mobiles to quiet mode when they are in these situations.

– Similarity between two user profiles• Pure similarity• Task-based user similarity

– Based on similarities between tasks they have previously accomplished in similar situations.


Methodology (cont. 2)

• Knowledge-Based Filtering– To overcome the problem of new users and new tasks

which is an inherent issue of the collaborative filtering.– Individual tasks are often associated with context (e.g.,

places and devices).– Therefore, construct task repositories oriented to

places and devices.– Task frequency, task sequences, task groups, and task

hierarchies are also good sources for prediction of relevant tasks.


Methodology (cont. 3)

• Utility-Based Filtering– Feasibility of a task • Defines the degree of feasibility to accomplish the task

in a given environment.• Calculated by matching required capabilities of the task

with provided capabilities of the environment.– Task Autonomy• Indicates to what extent the task must be

accomplished by the environment.• Calculated by the sum of autonomy degrees of basic

actions of the task.


System Architecture

• The system has five main components– Context Manager– Resource Manager• Responsible for discovery and management of available

devices and services in the environment– Task Execution Manager• Execute selected tasks and manage their executions

– Task Recommendation Engine (TRE)• Reason and recommend relevant tasks

– TASKREC Clients• Run on smart devices, act as UIs with environment.


Implementation• Use Socket technology for communication between

TRE and TASKREC Client.• Almost components are written in J2SE while TASKREC

Client is written using Java ME.• Consider a situation:– User = ‘Bob’– Time = ‘8am, Monday’– Weather = ‘Cold, rainy’– Place = ‘In front of the office’

Applying knowledge-based filtering reduce the universe of tasks (perhaps hundreds of tasks) to Office-related tasks and Mobile-related tasks.


Implementation (cont.)• Applying situation similarity-based collaborative filtering

reduced to 4 tasks.• These tasks are ranked based on their feasibility & autonomy (Fig. 1).• User can also specify preference of how each task can be recommended

in the future (Fig. 2).

Fig. 1 Fig. 2 13C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

Related work• Situation-aware application recommendation [Cheng et al. 2008]

– They recommend applications <> We recommend tasks (multi-apps)– They use pure situation similarity <> We use task based similarity

• Homebird system [Rantapuska et al. 2008]– It recommends tasks based on features of devices discovered– However, because this approach does not consider user situation, it can recommend feasible

tasks which may be not relevant.• InterPlay [Messer et al. 2006]

– For device integration and task orchestration in a networked home.– It asks user to express their intended tasks and assumes that the users have knowledge about

feasible tasks.– In contrast, our approach can recommend relevant, feasible tasks without these

requirements.• Context-dependent task discovery [Ni et al. 2006]

– Discovering active tasks by matching current context with required context of tasks.– This can discover feasible tasks but potentially irrelevant tasks.

• Task retrieval [Fukazawa et al. 2005]– Ask user to specify target names (e.g., cafe shop, theatre) for retrieving tasks which are

associated with these names.– Our system has integrated this knowledge into place/devices-related task repository.


Conclusion & Future work• Conclusion

– Introduced a context-aware task recommendation system for the complexity problem in smart spaces.

– Used collaborative filtering, knowledge-based filtering, task feasibility, and task autonomy.

– Presented a new measure for situation similarity and user similarity based on tasks.

• Future work– Complete Task Execution Manager– Build context-aware task models (for UI inconsistency problem)– Address the conflicts of recommendations in multiuser

environments.– Conduct a user study


ReferencesM. Weiser, “The computer for the 21st century,” Sci. American, 3(265), pp. 94–104, 1991.D. A. Norman, The Design of Future Things. Basic Books, 2007.Z. Wang & D. Garlan, “Task-driven computing,” School of Computer Science, Carnegie Mellon University, Tech.

Rep., 2000.D. Garlan et al. “Project Aura: toward distraction-free pervasive computing,” Pervasive Computing, IEEE, 1(2), pp.

22–31, 2002.R. Masuoka et al. “Task computing – the semantic web meets pervasive computing,” The SemanticWeb, pp. 866–

881, 2003.D. Magnusson & B. Ekehammar, “Similar situations–similar behaviors? a study of the intraindividual congruence

between situation perception and situation reactions,” J. of Research in Personality, 12, pp. 41–48, 1978.A. K. Dey, “Understanding and using context,” Per. and Ubi. Computing, 5(1), pp. 4–7, 2001.D. Cheng et al. “Mobile situation-aware task recommendation application,” in The 2nd Int. Conf. on Next

Generation Mobile App., Services, and Tech., 2008.A.Messer et al. “InterPlay: A middleware for seamless device integration and task orchestration in a networked

home,” in PERCOM’06. 2006, pp. 296–307.H. Ni et al. “Context-dependent task computing in pervasive environment,” Ubi. Comp. Sys., pp. 119–128, 2006.Y. Fukazawa et al. “A framework for task retrieval in task-oriented service navigation system,” in OTM Workshops

2005, pp. 876–885.O. Rantapuska and M. Lahteenmaki, “Homebird–task-based user experience for home networks and smart

spaces,” in PERMID 2008, 2008.

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