Rating Inference for Custom Trips from
Enriched GPS Traces using Random Forests
Theodoros Chondrogiannis

Mouzhi Ge

University of Konstanz

Deggendorf Institute of Technology

Germany

Germany

theodoros.chondrogiannis@uni-konstanz.demouzhi.ge@th-deg.de

7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising
Hamburg, Germany, November 13, 2023

Agenda• Motivation• Definition and Background• Problem statement and scope• Trip rating inference framework• Feature encoders• Experimental settings and results• Key take-aways

Motivation• GPS-enabled devices allow us to pinpoint our location and generate a large
amount of data that traces our movements along trips.
• Custom trips are designed to cater to travelers’ specific desires and user
preferences for personalized tourism experience.
• Since the custom trip is usually new in the system, no rating can be shown
to the user. As a result, the rating inference of custom trips has emerged as
an important feature in tourism applications and location-based services.
• This paper aims to determine which representation feeds best to themachine learning algorithms and achieves higher accuracy for rating
inference.
• Apart from trip recommendations, rating inference in this paper can beconsidered a second opinion for custom trips defined by users.

Custom trip

Enriched GPS traces along with the trip
• Trip location• Closeness to POIs

• Trip elevation• Closeness to places where users take pictures

Multi-criteria ratings for trips• Multi-criteria ratings consider different factors simultaneously. For
example, one hiking route would have various attributes for ratings,
such as Condition, Difficulty, Technique, Quality of Experience, andLandscape.
Condition
DifficultyTechnique
Quality of Experience
LandscapeProblem statement and scope• The user designs a custom trip
• This trip contains enriched GPS traces• There are different rating criteria for this trip
• We want to infer/predict• The rating of each criterion for this custom trip
• We want to use machine learning to do the rating inference, but the focusof this work is not ML model selection, it is feature engineering andencoding selections, given the enriched GPS traces are complex.

Trip rating inference frameworkLocation Encoder• We first impose a 𝑛 × 𝑛 grid over the space defined by the minimum
bounding rectangle of all traces.

• One-hot encoding• 𝑍-order curve to first ID the grids• For each set of IDs the trip crosses, a vector that contains basic statistics, i.e., min, max, mean, and median values.
• Histogram of 𝑛 buckets
0101

2

3

2

3

0
3

Altitude Encoder<• total ascent,• total descent• minimum altitude• maximum altitude• standard deviation of the elevation profile>

POI Distance Encoder• A combination of two vectors• Vector 1: Distances to all POIsPlus• Vector 2: Distances to a predefined set of POIs (𝑘 nearest POIs)

Geo-tagged Images Encoder• A bit vector and the size equals to the number of images in 𝐼
• we set each bit associated with an image to 1 if the minimum
distance between the trace and the image location is below apredefined threshold, e.g., 20 meters.

Datasets• Trip data obtained from Outdooractive: www.outdooractive.com• Elevation data for trip from Copernicus: www.copernicus.eu• 181,185 POI data from www.kaggle.com/datasets/ehallmar/pointsof-interest-poi-database• 50,000 geotagged images fromwww.kaggle.com/datasets/habedi/large-dataset-of-geotaggedimages

Encoding methods overview

ML Model and Evaluation Metric• Random forest• Our previous experiments demonstrated that Random Forest performs best in
several similar rating inference scenarios. We used Random Forest classifier inthis work.

• MAE, widely used for evaluating rating predictions, especially inrecommender system research.

Experimental Results

Take aways• "One size fits all" encodings may lower the quality of multi-criteria
rating inferences.
• Different encodings might be dynamically used to infer different
rating criteria.
• The trip-oriented ratings are focused on the intrinsic features of thetrip. Thus, the encoding of trip profiles can offer higher-quality rating
inferences.
• User-oriented ratings focus on how users feel about the trip and user
satisfaction.

Summary and Future Research• Scope of this research: encoding selection, not model selection.
• The model may consider more contextual factors. For example, thecontext of a trip may include group dynamics, previous experiences,and cultural factors.
• Users would often like to know how the inference is made. In turn,users can be more confident in their trip decisions. Therefore,
developing transparent and explainable models may increase user
trust and satisfaction.
• Including user feedback to enhance user engagement is also critical.
User feedback can be used to improve the model training and provide
continuous improvement for implementing trip recommendations

Thank you and questions