HAPORI: CONTEXT-BASED LOCAL SEARCH FOR MOBILE PHONES USING COMMUNITY BEHAVIORAL MODELING AND SIMILARITY Presented By: Brandon Ochs Nicholas D. Lane, Dimitrios Lymberopoulos, Feng Zhao, Andrew T. Campbell, Hapori: Context- based Local Search for Mobile Phones using Community Behavioral Modeling and Similarity, In Proc. of the 12th International Conference on Ubiquitous Computing, September 2010.

What does Hapori do?  Improves local search technology for mobile phones  Uses context to provide more relevant results  Incorporates behavioral modeling between user groups  Improves relevance of local search results by up to 10 times

What is local searching?  Use GPS data to provide a list of businesses associated with a query  Work best with a narrow range of queries where relevance is clear

How does Hapori improve on this?  Consider other factors such as time, weather, and activity of the user  Build behavioral models of users and exploit the similarity between user’s tastes  Consider emerging trends  Personalize responses for each user  Current prototype only uses context information that can be extracted from search logs

Key Components  Compute features that capture significant aspects of context  Learn customized ranking metrics that emphasize important traits in search category  Model differences between people  Adapt to changes in community behavior

Hapori In Use  Imagine a senior and a teenager located at the same position in a city on a hot day, and they happen to type the exact same search query: entertainment  Normally they would both be presented with the same information  However with Hapori the context and behavioral building models are taken into consideration, as well as the popular choices within the community  The teenager might be given the suggestion of a free rock concert outside in the park and the senior would be informed about a popular foreign movie in an air conditioned theater

Analyzing Search Log Content  Analyzed 80,000 local search queries submitted to Mobile Bing Local by more than 11,000 users  Data from search logs contained  Query terms  Unique identifier for the POI that is clicked  GPS location of user  The exact date and time the query was submitted  Unique user identifier  Time and date used to extract weather data

Context and Community Behavior  Identified traits in the POI (Point of Interest) selection  Identified community preferences between groups of people  This analysis of search log data was turned into the Hapori engine

Impact of Temporal Context  People’s behavior and activities vary depending on the day of the week and the time of day  Fast-food places, informal restaurants and local coffee shops ( ) chosen more on weekday mornings  The selection of these POI’s drop significantly during the weekday evenings and weekend

Impact of Weather Context  Weather is an important factor for activities (not just outdoors)  A walk in the park might be nice on a sunny day, but not when it’s raining  On cold days people prefer activities that are indoors  Some activities were observed to be popular across all conditions

Impact of Personal Context  Words like recreation and entertainment lead to different choices between groups of users  Subsets of users identified through heuristics (machine learning)  Context was shown to have the largest impact on the selected POI

Impact of Spatial Context  Takes popularity within the community into account  Tends to override other factors such as difficulty of travel or cost and quality  Bing query data showed that the closest businesses to the query location were not as popular as POIs that were further away

Hapori Framework Overview  Two key stages  Off-line model training process  On-line local search response  Assumes only a minimal form of query as input where user selects a category  Query is augmented with contextual information

Mining Community POI Decisions  User selection of a POI means that the result was satisfactory  POI decisions can also be mined by monitoring the actions of the user (not currently implemented)  Jogging at a particular track  Shopping at a specific store

Extract Contextual Features  For each mined POI decision a series of features are extracted  Currently Hapori searches for four features:  Temporal  Spatial  Weather  Popularity  These features represent different types of context that has strong influence over POI decisions

Temporal Features  The day of the week  Weekend/weekday  Which four-hour window POI selection occurred in

Spatial Features  Longitude and Latitude of source and destination  Tile of both source and destination  Tile counts of {10 2, 256 2, 512 2, }  Reduce importance of distance on weekends

Weather Features  Calculate weather statistics from the day the POI is made  Rainfall  Snowfall  Average temperature  Represented as separate features with different levels of discretion

POI Popularity Features  Two forms of popularity exist  Sharp spikes of interest  Stable POI preference  A POI is considered a spike in interest if it is a trend that is less than three weeks old

How do you determine community characteristics from the Temporal, Spatial, Weather, and Popularity features?

Computing Community Similarity  Calculate the differences between people through a community similarity metric  Uses a set of five features to determine community characteristics  Which four hour window they selected the POI  Day of the week  Their spatial location (512 2 tile)  POI category (hair dressing)  Specific POI (Joe’s hair design)

Learn POI Category Relevance Metrics  Metrics account for a POI having different criteria  Selecting a place to shop vs entertainment  Hapori learns a completely new metric for every POI category it supports  The model associated with the user is used for searching, while the community behavior helps during the POI ranking phase

Evaluation  Used the same 80,000 query data set to conduct an evaluation of Hapori  4,000 unique POIs  11,000 users  Data came from Seattle, WA  Spans January to July 2009  Ignore searches for specific POI such as “Starbucks”

Training Data  60,000 queries were used to train the POI preference model for Hapori  Remaining 20,000 queries are used to test the system  A rank score is calculated, which is the position of the POI selection within the ranked list  The lower the score, the better

POI Model Performance  Mobile Bing Local displays the correct POI in the first ten search results for 3,000 queries  Hapori achieves the same performance for 12,000 queries  Mobile Bing Local displays the correct POI in the first two search results for 900 queries  Hapori achieves the same performance for 9,000 queries

Feature Sensitivity  The most significant features are:  1) Temporal features  2) Community similarity features  3) Popularity and weather  Adding temporal features improved the average rank score by 11

Improvements On The Paper  Future work: Where is this going? What other features can be implemented to improve results?  Additional comparisons besides Mobile Bing Local needed. How does this compare to google?

Questions?

Conclusions  A major transformation of local search services is underway  Shifting from answering specific questions to broader ones  Hapori takes the first step in this direction

References  [1] Nicholas D. Lane, Dimitrios Lymberopoulos, Feng Zhao, Andrew T. Campbell, Hapori: Context-based Local Search for Mobile Phones using Community Behavioral Modeling and Similarity, In Proc. of the 12th International Conference on Ubiquitous Computing (Ubicomp), September  [2] Microsoft. Mobile Bing Local.