1. Towards Context-Aware Task Recommendation C.C. Vo, T. Torabi, and S. W. Loke La Trobe University 1 Presenter: Seng W. Loke

2. 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

3. 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 UIs 3C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

4. 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) 4C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

5. 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?” 5 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

6. 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 = (c 1,c 2,…,c n ) – (Time = ‘Monday’, Place = ‘Library’) is a situation. 6 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

7. 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. 7C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

8. 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. 8C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

9. 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. 9C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

10. 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. 10C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

11. 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 – T ASK R EC Clients Run on smart devices, act as UIs with environment. 11C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

12. 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. 12C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

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

14. 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. 14C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

15. 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 15C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

16. References M. 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 2 nd 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. 16

17. Question? Thank you! 17C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University