Identifying the Activities Supported by Lo-cations with Community-Authored Content

David Dearman and Khai N. TruongUniversity of Toronto

UbiComp’10

Summary

• Not to infer what activity a person is currently per-forming

• But to identify a set of potential activities that are supported by a person’s location

• Community-authored content = review texts on locations in Yelp.com

• Potential user activities = verb-noun pairs

E.g. “check zoo”, “take rowboat”, “play chess” for Cen-tral Park“play tennis”, “sit hill”, “drink beer” for Dolores Park“ride bike”, “walk dog”, “walk paths” in common

Activity in UbiComp

• Mainly focusing on inferring what activity a person is currently performing▫ Analyzing the physical manifestation of the base-level actions

• On-the-body sensing▫ Accelerometers, audio, barometric pressure, compass▫ Fairly robust, yet lack of generality▫ Good for differentiating a small set of activities

E.g. sitting, standing, walking, running, driving, biking, etc.

• Household activity sensing▫ RFID tags on household objects or 900 low-fidelity sensors in a

house Object-use fingerprints “making coffee”

▫ Sensors such as microphones at water fixtures (clothes washer, dishwasher, shower and toilet)

▫ Plug-in sensors detecting electrical events (light switches, electric stove, and TV)

Potential Activity Deriving Process

• (1) Harvesting the review texts and related attributes (e.g., date authored) for each unique location

Review texts + name, URL, latitude, longitude, #reviews

• (2) Parsing the review texts to identify each sentence Stanford Part-Of-Speech Tagger individual sentences

• (3) Tagging each word of a sentence with its part-of-speech and extracting local verb-noun pairs to form activities

Stanford Part-Of-Speech Tagger part-of-speech of each word Pairs of valid verb + nearest local noun

• (4) Populating and updating the activity database with the identified verb-noun pairs

WordNet respective base-word of verbs and nouns

Evaluation Results• Community-authored reviews, specifically Yelp reviews, are a diverse and

comprehensive data source that can be processed to identify the activi-ties supported by the reviewed location. With respect to the 40 most common verb-noun pairs we achieve a mean precision up to 79.3% and recall up to 55.9%.

• The number of reviews authored for a location has a significant impact on precision for the 40 most common verb-noun pairs. We achieved a mean precision up to 29.5% when processing only the first 50 reviews, increas-ing to 45.7% and 57.3% for the first 100 and 200 reviews, respectively.

• There is a significant difference in the activities that participants describe performing at a location and the 40 most common verb-noun pairs identi-fied for a location. The difference highlights the personal and individual nature of the provided activities in contrast to the verb-noun pairs identi-fied in the community-authored reviews.

Activity in NCLab

• SeeMon is a context monitoring framework for sensor-rich and resource-limited personal mobile environments. It is a highly scalable and energy-efficient framework, providing users with proactive services by monitoring their contexts continuously. More importantly, the framework runs efficiently in environments with limited computing and battery power. On top of the frame-work, multiple applications can simultaneously operate to under-stand the contexts of users and serve them appropriately.

• ActraMon is an Activity Travel Pattern (ATP) monitoring system in city environments by tracking the whereabouts of city residents and vehicles and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling.