lcars a location content aware recommender system n.
Skip this Video
Loading SlideShow in 5 Seconds..
LCARS: A Location-Content-Aware Recommender System PowerPoint Presentation
Download Presentation
LCARS: A Location-Content-Aware Recommender System

Loading in 2 Seconds...

play fullscreen
1 / 27

LCARS: A Location-Content-Aware Recommender System - PowerPoint PPT Presentation

  • Uploaded on

LCARS: A Location-Content-Aware Recommender System. 資工所  陳冠斌   P76024407 資工所  陳吉德   P76024114 資工所  陳昱琦   P76024295 醫資所  蔡有容   Q56021016. SIGKDD’13 Hongzhi Yin 、 Bin Cui 、 Zhiting Hu Peking University, Beijing, China Yizhou Sun Northeastern University, Boston, USA

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 'LCARS: A Location-Content-Aware Recommender System' - peyton

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
lcars a location content aware recommender system

LCARS: A Location-Content-Aware Recommender System

資工所  陳冠斌  P76024407

資工所  陳吉德  P76024114

資工所  陳昱琦  P76024295

醫資所  蔡有容  Q56021016


Hongzhi Yin、Bin Cui、Zhiting HuPeking University, Beijing, China

Yizhou SunNortheastern University, Boston, USA

Ling ChenUniversity of Technology, Sydney, Sydney, Australia

  • Introduction
  • Lacation-Content-Aware recommender system
    • Offline modeling
    • Online recommendation
  • Experiments
    • Settings&Comparative approaches
    • ResultsRelated work
  • Conclusion
introduction cont d
Introduction (cont’d)
  • Observation the travel mode of a user
    • only visit a limitednumber of physical venues

User-item sparse

    • New place: not have any activity history

Cold Start

  • To solve these problems, we add
    • Spatial item’s location
    • Content information (e.g. item tags or category words)
introduction cont d1
Introduction (cont’d)
  • Problem Definition
    • Given a querying useru with a querying citylu, find k interesting spatial items within lu, that match the preference of u
lcars offline modeling
LCARS-Offline Modeling

LCA probabilistic mixture generative model

the content information of spatial itemin LCA-LDA

lcars offline modeling cont d
LCARS-Offline Modeling ( cont’d)

We assume that items and their content words are independently conditioned on the topics.

lcars offline modeling cont d1
LCARS-Offline Modeling ( cont’d)

to estimate unknown parameters

{ θ, θ’, φ, φ’, λ} in the LCA-LDA

lcars online recommendation
LCARS-Online recommendation

Weight score

Offline scoring

  • Online recommendation part computes a ranking score
lcars online recommendation cont d
LCARS-Online recommendation( cont’d)

Compute Threshold Algorithm

experiments datasets
  • EBSN-DoubanEvent
  • LBSN-Foursquare
experiments comparative approaches
Experiments-Comparative approaches

User interest, social and geographical influences ( USG)

Category-based k-Nearest Neighbors Algorithm ( CKNN)

Item-based k-Nearest Neighbors Algorithm ( IKNN)


Location-Aware LDA ( LA-LDA)

Content-Aware LDA ( CA-LDA)

experiments evaluation methods
Experiments-Evaluation methods
  • 1st:
    • Test set => all spatial items visited by the user in a non-home city
    • Training set => the rest of user’s activity history in other cities
  • 2nd:
    • Test set => 20% of spatial item visited by the user in personal home city
    • Training set => the rest of personal activity history
experiments results effectiveness



Top-k Performance on DoubanEvent

experiments results effectiveness cont d
Experiments-Results_Effectiveness ( cont’d)

Top-k Performance on Foursquare

experiments results effectiveness cont d1
Experiments-Results_Effectiveness( cont’d)

Impact of the Number of Latent Topics

experiments results efficiency cont d
Experiments-Results_Efficiency ( cont’d)

Efficiency w.r.t Recommendations

  • Facilitates people’s travel
    • Not only in their home area but also in a new city where they have no activity history
  • Takes advantage of both the content and location information of spatial items
    • Overcomes the data sparsity problem in the original user-item matrix
  • Will the results be different if they use other evaluated methods? (Because this paper just use recall@k to evaluate the effectiveness)
    • if they use other methods to evaluate the recommendation system, the results may not be as good as they used recall@k

Discussion( cont’d)

  • As prof. Tseng ask, where is the difference of dataset between figure 3(a)(b)?
    • Figure 3(a) is the result of the 1st method(ppt page p.16) that divide dataset into testing set and training set
discussion cont d
Discussion( cont’d)
  • How’s the results between figure 3(a) and figure 3(b)? Is figure 3(b) better than (a) just because (b)denotes users traveling in home cities?
    • We can find out the result that (b) is better than (a). However, the distribution of dataset of the two experiments are different. They use different training set to train the model, so there is no basis for comparison
discussion cont d2
Discussion( cont’d)
  • What will we do if we want to compare the difference between querying new cities and querying home cities?
    • We will use the same dataset to train the offline model and estimate the parameters