Post on 13-Apr-2017
transcript
ENTER 2016 Research Track Slide Number 1
Contextual Information Elicitation in Travel Recommender Systems
Matthias Braunhofer and Francesco Ricci
Free University of Bozen - Bolzano, Italy{mbraunhofer,fricci}@unibz.it
http://www.inf.unibz.it
ENTER 2016 Research Track Slide Number 2
Agenda
• Introduction• Related Work• Selective Context Acquisition• Experimental Evaluation and Results• Conclusions
ENTER 2016 Research Track Slide Number 4
Context-AwareRecommender Systems (CARSs)
• CARSs provide better recommendations by incorporating contextual information (e.g., time and weather) into the recommendation process
STS (South Tyrol Suggests)
ENTER 2016 Research Track Slide Number 5
Context Acquisition Problem of CARSs
• How to identify and acquire the truly relevant contextual factors that influence the user preferences and decision making process?
ENTER 2016 Research Track Slide Number 6
STS w/o Selective Context Acquisition
We can’t elicit the conditions for all the available contextual factors when the user rates a POI.
ENTER 2016 Research Track Slide Number 7
STS w/ Selective Context Acquisition
Rather, we must elicit the conditions of a small subset of most important contextual factors.
ENTER 2016 Research Track Slide Number 9
Context AcquisitionProblem in Commercial Systems• Numerous commercial systems in the tourism
domain face the context acquisition problem
TripAdvisor Foursquare
ENTER 2016 Research Track Slide Number 10
A Priori Context Selection
• Web survey in which users evaluate the influence of contextual conditions on POI categories
• Allows to identify the relevant factors before collecting ratings
(Baltrunas et al., 2012)
ENTER 2016 Research Track Slide Number 11
A Posteriori Context Selection
• Several statistic-based methods for detecting the relevant context after collecting ratings
• Results show a significant difference in prediction of ratings in relevant vs. irrelevant context (Odić et al., 2013)
ENTER 2016 Research Track Slide Number 13
Parsimonious and Adaptive Context Acquisition
• Main idea: for each user-item pair (u, i), identify the contextual factors that when acquired with u’s rating for i improve most the long term performance of the recommender– Heuristic: acquire the contextual factors that have the
largest impact on rating prediction• Challenge: how to quantify these impacts?
ENTER 2016 Research Track Slide Number 14
CARS Prediction Model
• We use a new variant of Context-Aware Matrix Factorization (CAMF) (Baltrunas et al., 2011) that treats contextual conditions similarly to either item or user attributes
Latent vector of item i Latent vector of user u
Latent vectors of conventional (e.g., genre)
and contextual item attributes (e.g., weather)
Avg. rating for item i
Bias of user uLatent vectors of conventional (e.g., age)
and contextual user attributes (e.g., mood)
ENTER 2016 Research Track Slide Number 15
Largest Deviation
• Given (u, i), it computes a relevance score for each contextual factor Cj by first measuring the “impact” of each contextual condition cj C∈ j:
• Finally, it computes for each factor the average of these deviation scores, and selects the contextual factors with the largest average scores
Normalized freq. of cj
Rating prediction when cj holds
Predicted context-free rating
ENTER 2016 Research Track Slide Number 16
Illustrative Example
• Let ȓAlice Skiing Sunny = 5, ȓAlice Skiing = 3.5 & fSunny = 0.2. Then, the impact of the “Sunny” condition is:– ŵAlice Skiing Sunny = 0.2 · |5 - 3.5| = 0.3
• Let ŵAlice Skiing Cloudy= 0.2, ŵAlice Skiing Rainy= 0.3 &ŵAlice Skiing Snowy= 0.1, the impacts of the other weather conditions. Then, the overall impact of the “Weather” factor is: – (0.3 + 0.2 + 0.3 + 0.1) ÷ 5 = 0.18
ENTER 2016 Research Track Slide Number 18
DatasetsDataset STS TripAdvisor
Rating scale 1-5 1-5
Ratings 2,534 4,147
Users 325 3,916
Items 249 569
Contextual factors 14 3
Contextual conditions 57 31
Avg. # of factors known for each rating 1.49 3
User attributes 7 2
Item attributes 1 12
In STS when a user rates a POI she commonly
specifies at most 4 out of the 14 factors!
ENTER 2016 Research Track Slide Number 19
Evaluation Procedure: Overview• Repeated random sampling (20 times):
– Randomly partition the ratings into 3 subsets
– For each user-item pair (u, i) in the candidate set, compute the N most relevant contextual factors and transfer the corresponding rating and context information ruic in the candidate set to the training set as ruic’ with c’ c containing the conditions for these factors, if any⊆
– Measure prediction accuracy (MAE) and ranking quality (Precision) on testing set, after training the prediction model on extended training set
– Repeat
Training set (25%) Candidate set (50%) Testing set (25%)
ENTER 2016 Research Track Slide Number 20
user-item pair
Evaluation Procedure: Example
(Alice, Skiing)
Season and Weather
rAlice Skiing Winter, Sunny, Warm, Morning = 5
rAlice Skiing Winter, Sunny = 5
top two contextual factors
rating in candidate set
rating transferred to training set
+
+
=
ENTER 2016 Research Track Slide Number 21
Baseline Methods for Evaluation
• Mutual Information: given a user-item pair (u,i), computes the relevance for a contextual factor Cj as the mutual information between ratings for items belonging to i’s category (Baltrunas et al., 2012)
• Freeman-Halton Test: calculates the relevance of Cj using the Freeman-Halton test (Odić et al., 2013)
• mRMR: ranks each Cj according to its relevance to the rating variable and redundancy to other contextual factors (Peng et al., 2005)
ENTER 2016 Research Track Slide Number 22
Evaluation Results: Prediction Accuracy
ENTER 2016 Research Track Slide Number 24
Evaluation Results: # of Acquired Conditions
ENTER 2016 Research Track Slide Number 26
Conclusions
• Using Largest Deviation, we know that we can ask only the contextual factors C1, C2 and C3 when we ask user u to rate item i