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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. - PowerPoint PPT Presentation
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An Energy-Efficient An Energy-Efficient Mobile Mobile
Recommender Recommender SystemsSystems
An Energy-Efficient An Energy-Efficient Mobile Mobile
Recommender Recommender SystemsSystems
Bingchun ZhuBingchun ZhuDung PhanDung Phan
Hien LeHien Le
February 22, 2011
Recommender Systems
Agenda
• Introduction– Recommender System(RS)– Motivation– Problem formulation
• Algorithm• Experiment Results• Conclusion
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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
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Types of RS
Three broad types:
1. Content based RS2. Collaborative RS3. Hybrid RS
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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
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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
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Types of RS: Hybrid
Hybrid model
The combination of two above models
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MOBILE Recommender System
IS widely studied before
BUT - Mostly based on user ratings - and is only exploratory in natureSOUnique features distinguishing mobile RS
remains open
The combination of two above models
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MotivationTaxi 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
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To
• Provide more useful “local navigation” options
• High density of customer looking for the Cab
THEN:• Potential Travel Distance(PTD)• LCP• Skyroute algorithm
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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
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Problem Formulation
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Problem Formulation
• Assume a available set of N potential pick-up points:C = {C1, C2…Cn}
And • P={P(C1), P(C2)…, P(Cn)} is the
probability set, where P(Ci) is estimated probability at each pick-up point.
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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
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Mobile Sequential Recommendation(MRS) Problem
• is PTD function
• Driver current position
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Problem Formulation
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Computational Complexity
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Sequential Recommendation Algorithm
• Potential Travel Distance Function (PTD)– an objective function which is used to eval
uate 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
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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 hap
pens at c1, this event happens.• ...• c4: a pick-up event happnes with pro
bability (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
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Potential Travel Distance Function
ii
L
iiR
L
iiLR
pp
where
DDDDD
pppppP
1
,...,,
,...,,
1211
1
1211
PTD is defined as the expected distance for a cab before picking up a customer in the route RL:
...)21(2)11(11 DDppDpDPPTD RR
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PTD Function Property
• Lemma. The Monotone Property of the PTD Function– the PTD function is strictly monotonically i
ncreaseing 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.
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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
By this definition, if a candidate route A is dominated by a candidate route B, A cannot be an optimal route.
element 2 B dominates A
B dominates C
element 1
A
B C
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Constrained Sub-route Dominance
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LCP Pruning Algorithm
• LCP Pruning algorithm– For two sub-routes A and B with a
length L , which includes only pick-up points, if sub-route A is dominated by sub-route B under Definition 2, the candidate routes with a length L which contain sub-route A will be dominated and can be pruned in advance.
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• LCP algorithm prunes the search space offline– LCP algorithm will enumerate all the L-len
gth sub-routes;– then prune the dominated sub-routes by
difinition 2 offline.• this pruning process can be done offline be
fore the position of a taxi driver is known
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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.
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Backward Pruning
C5 C6 C7PoCab
R1
R2
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SkyRoute Algorithm• Input
C: set of pick-up pointsP: probability set for all pick-up pointsDist: pairwise drive distance matrix of pick-up p
ointsL: the length of suggested drive routePoCab: current position
• Offline Processing (LCP)– Enumerate all sub-routes with length of L fro
m 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
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• Online Processing– Enumate all candiate routes by connecting P
oCab 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 l
ength of L from the loop above– Final typical skyline query to get optimal sky
line routes
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Keywords
• 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
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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?
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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
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Experimental Data
• Real world data: - GPS location traces of approximately 500
taxis collected around 30 days in San Francisco Bay area - Number of pick-up
points: 10 - Travelling distances
between pick-up points are measured with Google Map API
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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
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Optimal Routes with Real World Data
L=3: → C1 → C3 → C2 L=4: → C1 → C3 → C2 → C7
L=5: → C4 → C1 → C2 → C3 → C7
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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
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Experiment Results
• A Comparison of Search Time - LCPS overperforms BFS and
SR(D&C)S
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Experiment Results(cont.)
• Comparison of Search Time(L=3) on Synthetic Data Set
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Experiment Results(cont.)
• The pruning effect
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Experiment Results(cont.)
• Comparison of Skyline Computing
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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
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Multi Evaluation Functions(cont.)
• Using 5 different evaluations (including PTD)
• Select 5 corresponding optimal drive routes
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Conclusion
• 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.
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THANK YOU !!!
Questions??