HAPORI: CONTEXT-BASED LOCAL SEARCH FOR MOBILE PHONESUSING 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 (200-300) 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 20-30 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 {102, 2562, 5122, 10242}

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 (5122 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 2010.

[2] Microsoft. Mobile Bing Local. http://m.bing.com/.