A Platform for Location Aware Service -- with human computation Ling-Jyh Chen, Meng Chang Chen Ming-Syan Chen, Sheng-Wei Chen, Jan-Ming Ho, Wang-Chien Lee Jane Liu, De-Nian Yang Research Center for Information Technology Innovation & Institute of Information Science Academia Sinica

Itinerary Recommendation System with Human Computation 2

Other Travel Apps in Handheld Devices Nearby Spots (LBS) –Android Pocket Journey –Android Wikitude –Garmine Static Travel Routes (ebook) –Garmine –MioMap –TomTom 3

Our Recommendation System A data mining approach with GPS to provide “route” or “itinerary” based LBS Main characteristics –Personalization –Human computation –Quick and Dynamic Mining Main Concept System Architecture GPRS/WiFi/…

Recommendation Server Kernel Modules –MSTravel An mining algorithm to discover user movement regularity (itinerant patterns) from itinerary datasetAn mining algorithm to discover user movement regularity (itinerant patterns) from itinerary dataset –Weight Grade A grading function to select top-k suitable patterns for rendering User Travel Log Recommendation Recommendation Server

Itinerant Patterns Mining Spirits –Inherent from association rule mining and sequence pattern mining Modeling itinerant pattern as a tuple (V, C, R) –V is an unordered set of visited scenic spots –C is the current location –R is an ordered sequence of recommended scenic spots –EX: (AB, C, DEF) Definition of Itinerant Patterns Mining Problem Given a itinerary dataset, discovering all itinerant patterns with popularity ≧ minimal ratio r min and frequency ≧ minimal threshold t min

Itinerant patterns vs. Sequential Patterns Itinerant patternsItinerant patterns –Prune irrelevant sequences Render local characteristics Provide more knowledge for recommendation Low computing complexity Sequential PatternsSequential Patterns –Consider all sequences Blur important local characteristics High computing complexity popularity popularity of (A,B,C) = # of itineraries contain *A*B*C* / # of itineraries contain *A*B* =|{2}|/|{2,3}| = 0.5 frequency frequency of (AB,C, EG) = # of itineraries contain *A*C* or *B*C* =|{1, 2}| =2 Ex.

Itinerant Patterns Mining Algorithm -- MSTravel A Recursive Approach –Explore k-1 Itinerant patterns –k-candidate Generation –Popularity-Testing against the minimal ratio and minimal threshold –Redundancy-Elimination prunes shorter itinerant patterns that are covered by the new discovered ones Advantages of MStravel –Prune irrelevant itineraries → reduce DB scan –Utilize apriori property in candidate generation → reduce the amount of comparisons in testing

Itinerant Patterns Recommendation Multiple relevant patterns Which one to recommend? How to rank the patterns? or

Weight Grading Possible recommendation strategies –Random k patterns, most popular k patterns, longest k patterns, … Our solution –Weighting to obtain top-k patterns Consider popularity and frequency of a patterns Consider similarity of a pattern and user’s visited spots F = w 1 * popularity + w 2 * frequency + w 3 * Jaccard (V, S) +w 4 * Jaccard (R, S) w 3 * Jaccard (V, S) +w 4 * Jaccard (R, S) Advantage: K can be designated according to various applications w 1 ~w 4 : weight factors, S: user’s visited spots

Client-end Design Web-based UI –Incorporated with Google Map –Simple operation –User friendly Mapping of a geographic coordinates (x,y) and a scenic spot –Positioning accuracy and multiple hot spots in the same location → not easy to identify user’s visited spot –List top-k near by spots or list top-k popular spots → users select manually GPS 定位 Windo ws SDK GPS API HTC Gensor API 手機上、下載路 徑 Windo ws mobile 6 SDK GPRS/ 3G 地圖顯 示 Web- based Google Map API

Client: Web-based