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Online hotel searching is a daunting task due to the wealth of online information. Reviews written by other travelers replace the word- of-mouth, yet turn the search into a time consuming task. Users do not rate enough hotels to enable a collaborative filtering based rec- ommendation. Thus, a cold start recommender system is needed. In this work we design a cold start hotel recommender system, which uses the text of the reviews as its main data. We define con- text groups based on reviews extracted from TripAdvisor.com and Venere.com. We introduce a novel weighted algorithm for text min- ing. Our algorithm imitates a user that favors reviews written with the same trip intent and from people of similar background (na- tionality) and with similar preferences for hotel aspects, which are our defined context groups. Our approach combines numerous ele- ments, including unsupervised clustering to build a vocabulary for hotel aspects, semantic analysis to understand sentiment towards hotel features, and the profiling of intent and nationality groups. We implemented our system which was used by the public to conduct 150 trip planning experiments. We compare our solution to the top suggestions of the mentioned web services and show that users were, on average, 20% more satisfied with our hotel recom- mendations. We outperform these web services even more in cities where hotel prices are high.
Citation preview
Cold Start Context-Based Hotel Recommender
SystemAsher Levi, Osnat (Ossi) Mokryn
Christophe Diot, Nina Taft
Hotel Domain• A user cold start problem• Contextual information• Domain data (Venere, TripAdvisor)
• Metadata (name, price, location)• Reviews – anonymous
• Text, trip intent, nationality
• Ratings • Over 87% of the ratings are in the range of [3-5]
• 3800 hotels, and 140000 reviews
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Can you guess ratings from reading reviews?
Count Average Difference Rate Difference
1474 (39.7%) 0.94 Estimation > Rate
2241 (60.3%) 1.67 Estimation < Rate
3715 (100%) 1.38 Total
• Mechanical Turk workers estimations. • 50 reviews, 3715 estimations
The hotel was really dirty, the room was small, the location was bad but the staff was great…
3?2?1?
In a Nutshell• We know that:
• Users are generous with the star ratings while expressing their real opinion in writing
• Previous visits might have different intents• in different context a user might rate the
same hotel differently
• Do the context groups have different needs?
• Can we identify them?
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Can we couple text analysis and user context to yield a better recommendation?
Common Traits• A trait in psychology is a basic characteristic of a
person• Introvert vs. extravert
• Common traits • Chinese year of birth determines a persons’ traits – for a group
of people
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We defined common traits in text
• Common Traits are typical words that appear more in text written within that context
For each context group cFor each feature fif > stdv(f) thenf -> common trait for context group c = frequency of feature f for context group c
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Feature weight• For each feature we assign a weight that
reflects its importance for each context group.
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Common Traits • Examples of common traits per group:
• Single traveller: wifi, tv, price, supermarket. • Family: air condition, car, space, shuttle, breakfast. • Group: bar, money, bus stop, shopping, party. • Couple: coffee, view, balcony, breakfast. • Business: Internet, park, bar, shopping.
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• Preferences for different hotel aspects• Room, Location, Service etc.
• Cluster features that relate to each aspect• Unsupervised Community Detection - Spin Glass
User Preferences
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V = Feature; E = Feature co-occurrence; = (
Spin Glass Communities
Location
Facilities
Room
Service
Experience
Food
Number of communities is determined by the algorithm
Communities sizes differ, and are also determined by algorithm
04/09/2023
BUILDING A PERSONALIZED
HOTEL SCORE
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Output is ranked order list of hotels
Assign weights to features for each
intent
Assign weights to features per nationality
Cluster hotels features to aspects
Build opinion lexicon with orientation
Text reviews wordnet
Preprocessing
Building personalized score
Select relevant feature weight for
intent
User intent
Select relevant feature weight for
nationality
User nationality
For each aspect, take features in that cluster and
assign weight
User preferences
Build feature weight
Build sentence, review score
Build final hotel score
Give semantic orientation for feature
User Input:
User’s Hotel Score• User select
• Purpose of the trip• Nationality • Aspect preference
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Feature weight Based Scoring
• Combine the features weights
weight of the purpose of the trip
weight of the nationality
weight of the users’ aspect preference
• The weights for each context are multiplied to allow fine grained differentiation of users within our various groups
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ExampleBathroom Weight = 1
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Room Location
Bathroom Weight = 1224 2
Bathroom
Alice Bob
Hotel Orientation Score
= hotel orientation score for user u
The feature’s score is the semantic orientation score multiply by it’s weight16
Bias Adjustment
= + + = bias of a user with intent p and nationality n, for hotel h
Bias of hotel h: = Bias of hotel h for purpose group p: = Bias of hotel h for nationality n : = • Hotel orientation score [-40 – 80] • Bias terms [0 – 5]• Bias objective is to break ties17
Hotel Score
Hotel (h) score for user (u):
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Validation• Verify the usefulness of nationality and bias
• Queries to the system with the tested parameter and without it• Number of queries executed was 2500• Calculate the distance for each query result (Jaccard distance)
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Parameter Top 10 Top 20
Nationality 16.6% 15%
Bias Score 9% 8%
Evaluation
• Human evaluation
• We present the user a list of six hotels• Recommendation from our system• Top rated hotels from Tripadvisor• Random order
• We obtained 150 evaluations
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Evaluation
• For each hotel in the results the user answered:
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Evaluation Results
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Would you select this hotel?
Evaluation Results
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How well is this recommendation matching your expectations?
Conclusions• Mechanical Turk experiment show that text caries
more information then ratings• Common traits can be found by pre-processing
large samples of text• With the use of traits we improved
recommendations• Future uses:
• Can group traits help identify whether an individual belongs to a group?
• Can a typical user per product be identified?
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Cold Start Context-Based Hotel Recommender
System
Asher Ossi Christophe Nina
Thanks! Questions?