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?