an energy efficient mobile recommender systems
Skip this Video
Download Presentation
An Energy-Efficient Mobile Recommender Systems

Loading in 2 Seconds...

play fullscreen
1 / 44

An Energy-Efficient Mobile Recommender Systems - PowerPoint PPT Presentation

  • Uploaded on

February 22, 2011. An Energy-Efficient Mobile Recommender Systems. Bingchun Zhu Dung Phan Hien Le. Agenda. Introduction Recommender System(RS) Motivation Problem formulation Algorithm Experiment Results Conclusion. RS: What are they and Why are they.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' An Energy-Efficient Mobile Recommender Systems' - otylia

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
  • Introduction
    • Recommender System(RS)
    • Motivation
    • Problem formulation
  • Algorithm
  • Experiment Results
  • Conclusion
rs what are they and why are they
RS: What are they and Why are they
  • RS: identify user interests and provide personalized suggestions.
  • Enhance user experience
    • Assist users in finding information
    • Reduce search and navigation time
  • Increase productivity
  • Increase credibility
  • Mutually beneficial proposition
types of rs
Types of RS

Three broad types:

  • Content based RS
  • Collaborative RS
  • Hybrid RS
types of rs content based rs
Types of RS – Content based RS

Content based RS highlights

  • Recommend items similar to those users preferred in the past
  • User profiling is the key
  • Items/content usually denoted by keywords
  • Matching “user preferences” with “item characteristics” … works for textual information
types of rs collaborative rs
Types of RS – Collaborative RS

Collaborative RS highlights

  • Use other users recommendations (ratings) to judge item’s utility
  • Key is to find users/user groups whose interests match with the current user
  • More users, more ratings: better results
  • Can account for items dissimilar to the ones seen in the past too
types of rs hybrid
Types of RS: Hybrid

Hybrid model

The combination of two above models

mobile recommender system
MOBILE Recommender System


widely studied before


- Mostly based on user ratings

- and is only exploratory in nature


Unique features distinguishing mobile RS remains open

The combination of two above models


Taxi services are very popular and:

  • Energy consumption counted;
  • Successful story of drivers are different
  • Data related to individuals and objects are rich
  • Mobile RS provides users access to personalized recommendation anytime, anywhere
  • Provide more useful “local navigation” options
  • High density of customer looking for the Cab


  • Potential Travel Distance(PTD)
  • LCP
  • Skyroute algorithm
problem definition
Problem Definition
  • Mobile sequential recommendation problem, which recommends sequential pickup points for Taxi driver to maximize his business success.
  • Recommend a travel route for a Cab driver in a way such that the potential travel distance before having customer is minimized
problem formulation1
Problem Formulation
  • Assume a available set of N potential pick-up points:

C = {C1, C2…Cn}


  • P={P(C1), P(C2)…, P(Cn)} is the probability set, where P(Ci) is estimated probability at each pick-up point.
problem formulation2
Problem Formulation

is the set of directed sequences

is the number of all possible driving routes

where is the length of route

is the probabilities of all pick-up points containing in

mobile sequential recommendation mrs problem
Mobile Sequential Recommendation(MRS) Problem
  • is PTD function
  • Driver current position
sequential recommendation algorithm
Sequential Recommendation Algorithm
  • Potential Travel Distance Function (PTD)
    • an objective function which is used to evaluate condidate routes
    • property of PTD
  • LCP algorithm
    • an algorithm which is used for pruning the search space offline
  • SkyRoute algorithm
    • an algorithm for seeking optimal recommendation routes
recommended driving route
Recommended Driving Route
  • c1: a pick-up event happens with probability p(c1)
  • c2: a pick-up event may happen with probabolity (1-p(c1))p(c2)
    • only when no pick-up event happens at c1, this event happens.
  • ...
  • c4: a pick-up event happnes with probability (1-p(c1))(1-p(c2))(1-p(c3))p(c4)

An exapmel of Recommended Driving Route with the length of suggensted driving route L = 4

potential travel distance function
Potential Travel Distance Function

PTD is defined as the expected distancefor a cab before picking up a customer in the route RL:

ptd function property
PTD Function Property
  • Lemma. The Monotone Property of the PTD Function
    • the PTD function is strictly monotonically increaseing with each attribute of vector DP.
      • Vector DP is a vector combined by vector D and vector P
  • With this property, it’s possible to determine a candidate route is better than the other without computing PTDs.
ptd function property1
PTD Function Property
  • A recommended driving route R1 with a length L, associated with the vector DP1, dominates another route R2 with a length L, associated with vector DP2, iff the following two conditions hold:
    • every element in DP1 is not worse than it peer in DP2
    • at lease element in DP1 is better than its peer in DP2

element 2

B dominates A

B dominates C

By this definition, if a candidate route A is dominated by a candidate route B, A cannot be an optimal route.




element 1

lcp pruning algorithm
LCP Pruning Algorithm

LCP Pruning algorithm

For two sub-routes Aand B with a length L , which includes only pick-up points, ifsub-route A is dominated by sub-route B under Definition 2,the candidate routes with a length L which contain sub-routeA will be dominated and can be pruned in advance.

LCP algorithm prunes the search space offline

LCP algorithm will enumerate all the L-length sub-routes;

then prune the dominated sub-routes by difinition 2 offline.

this pruning process can be done offline before the position of a taxi driver is known

skyroute and its property
SkyRoute and its Property

With lemma 4, if we can find skyline routes first, and then search the optimal driving routes from the set of skyline routes. This way can eliminate lots of candidates without computing the PTD function.

backward pruning
Backward Pruning







skyroute algorithm
SkyRoute Algorithm


C: set of pick-up points

P: probability set for all pick-up points

Dist: pairwise drive distance matrix of pick-up points

L: the length of suggested drive route

PoCab: current position

Offline Processing (LCP)

Enumerate all sub-routes with length of L from C

Prune and maintain dominated Constrained Sub-routes with length L using sub-route dominance.

Maintain the remained non-dmonated sub-routes with length L, denoted as

Online Processing

Enumate all candiate routes by connecting PoCab with each sub-route of

for i = 2: L-1

decide dominated sub-routes with i-th intermediate pick-up points and prune the corresponding candidates by using Backward pruning.

update the candidate set by filtering the pruned candidates in above step.

end for

Select the remained candidate routes with length of L from the loop above

Final typical skyline query to get optimal skyline routes

  • PTD function:

- a function to compute the Potential Travel Distance before having a customer

  • LCP algorithm:

- a route pruning algorithm.

- can be done offline before the position of a cab is known

  • SkyRoute algorithm:

- a route pruning algorithm

- SkyRoute includes:

+ LCP offline pruning

+ Online pruning when the position of a cab is known

recommendation process
Recommendation Process
  • Obtaining the Optimal Driving Route:

- Using LCP and SkyRoute for pruning candidates

- Compute PTD function for all remaining candidates

- Get the route with minimal PTD value

  • Other challenge: How to make the recommendation for many cabs in the same area?
recommendation process cont
Recommendation Process(cont.)
  • Circulating mechanism

- search k optimal drive routes

- NO.1 route to the 1st coming empty cab

- NO.2 route to the 2nd coming empty cab

- …

- More than k empty cabs? Repeat from NO.1

experimental data
Experimental Data
  • Real world data:

- GPS location traces of

approximately 500 taxis

collected around 30 days

in San Francisco Bay


- Number of pick-up points: 10

- Travelling distances between pick-up points are measured with Google Map API

experimental data cont
Experimental Data(cont.)
  • Synthetic data:

- Randomly generate pick-up points within a specific area

- Generate pick up probability by a standard uniform distribution

- Using Euclidean distance instead of driving distance

- 3 sets: 10, 15, 20 pick-up points respectively

optimal routes with real world data
Optimal Routes with Real World Data

L=3: → C1 → C3 → C2

L=4: → C1 → C3 → C2 → C7

L=5: → C4 → C1 → C2 → C3 → C7

evaluated algorithms
Evaluated Algorithms
  • BFS(Brute Force Search):

- Compute the PTD value for all candidate routes

- Find the minimum value as the optimal route

  • LCPS (LCP Search)

- Use LCP algorithm for offline pruning

- Compute PTD for remained candidate routes

- Get the minimum value as the optimal route

  • SR(BNL)S: Sky Route + BNL (Block Nested Loop)

- Using SkyRoute algorithm for pruning

- Applying BNL for the remained candidates to get skyline routes

  • SR(D&C)S: SkyRoute + D&C (Divide and Conquer )

- SkyRoute algorithm for pruning

- D&C algorithm to get skyline routes

experiment results
Experiment Results
  • A Comparison of Search Time

- LCPS overperforms BFS and SR(D&C)S

experiment results cont
Experiment Results(cont.)
  • Comparison of Search Time(L=3) on Synthetic Data Set
experiment results cont1
Experiment Results(cont.)
  • The pruning effect
experiment results cont2
Experiment Results(cont.)
  • Comparison of Skyline Computing
multi evaluation functions
Multi Evaluation Functions
  • Skyline computing is time consuming
  • Given a cab and fixed potential pick-up points:

- Skylines are needed to compute only one time

- Search space is pruned drastically

=> Skyline computing will have advantage with multi evaluation criteria

multi evaluation functions cont
Multi Evaluation Functions(cont.)
  • Using 5 different evaluations (including PTD)
  • Select 5 corresponding optimal drive routes
  • This paper developes an energy-efficient mobile recommender system for Taxi drivers. This system is able to recommend a sequence of potential pick-up points for a driver such that the potential travel distance before having customer is minimized.
  • This paper provides a Potential Travel Distance(PTD) function for evaluating candidate sequences and two recommendation algorithms LCP and SkyRoute.
  • LCP algorithm outperforms BFS and SkyRoute when searching for one optimal route. However, SkyRoute has better performance than BFS and LCP when there is an online demand for different optimal driving routes.