User-Oriented Context Suggestion

Yong Zheng, Bamshad Mobasher, Robin BurkeCenter for Web Intelligence

DePaul University, Chicago

The 24th Conference on User Modeling, Adaptation and PersonalizationHalifax, Canada, July 13-16, 2016

Question: Is it enough to have appropriate/good

item recommendations?

Zoo Parks in San Diego, USA

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• San Diego Zoo • San Diego Zoo Safari Park

Zoo Parks in San Diego, USA

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Intro: Context Suggestion

Traditional Recommender Systems

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• Task: Suggest a list of items to a user

• For example, recommend me a list of movies to watch

Traditional Rec

Context-aware Recommendation

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• Task: Suggest a list of items to a user in specific contexts

• For example, recommend me some movies to watch with my girlfriend at weekend in the cinema

Contextual RecTraditional Rec

Context Suggestion

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• Task: Suggest a list of contexts to users/items

• For example: suggest me the time/location to watch a movie

Context Rec

Contextual RecTraditional Rec

What is Context?

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• Our definition:

Contexts are those variables which may change when a same

activity is performed repeatedly – not only the time & location,but also companion, occasions, user intent/purpose, etc

• Examples:

Watching a movie: time, location, companion, etc

Listening to a music: time, location, emotions, occasions, etc

Party or Restaurant: time, location, occasion, etc

Travels: time, location, weather, transportation condition, etc

Motivations

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Context Suggestion: Motivations

• Motivation-1: Maximize user experience

User Experience (UX) refers to a person's emotions and

attitudes about using a particular product, system or

service.

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Context Suggestion: Motivations

• Motivation-1: Maximize user experience

It is not enough to recommend good items only

Good item recommendations cannot guarantee the whole user experience!

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Context Suggestion: Motivations

• Motivation-2: Contribute to Context Collection

Predefine contexts and suggest them to users

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Context Suggestion: Motivations

• Motivation-3: Connect with Context-aware RecSys

User’s actions on context is a context-query to system

Applications

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Context Suggestion: Applications

• There could be many potential applications:

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Context Suggestion

• There could be many applications, we focus on two tasks

1).UI-Oriented Context Suggestion

Task: suggest contexts to <user, item>

Example: time & location for me to watch Life of Pi

Existing solutions: Multi-label classification/predictions

2). User-Oriented Context Suggestion

Task: suggest contexts to each user

Example: Google Music, Pandora, Youtube, etc

Solution: this paper in UMAP 2016

Challenges and Solutions

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Challenge: Evaluations

1).UI-Oriented Context Suggestion

Task: suggest contexts to <user, item>

Example: time & location for me to watch Life of Pi

2). User-Oriented Context Suggestion

Task: suggest contexts to each user

Example: Google Music, Pandora, Youtube, etc

Same challenge: Evaluations!!

We do not have user’s preferences on contexts. No data!

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Evaluation: Solutions

In this paper, we use a simulation-based approach.

User’s taste on contextconditions can be obtained

by the average rating oncontext condition by usersacross contextual ratings

over all rated items.

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Algorithms: User-Oriented Context Suggestion

Solution 1). By Contextual Rating Deviations (CRDs)

CRD is used to tell how user’s rating is deviated in each context condition. For example, CRD(u, weekend) = 0.5,it tells that user u’s rating on items is usually higher by 0.5if watching movies at weekend

CAMF_C:

CAMF_CU:

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Algorithms: User-Oriented Context Suggestion

Solution 2).By UI-Oriented Context Suggestion

Color, Shape, Weight, Origin,Taste, Price, Vitamin c

Predictions By UI-Oriented Context Suggestion

Converted User-Context Predictions

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Algorithms: User-Oriented Context Suggestion

Solution 2).By UI-Oriented Context Suggestion

We choose two methods in UI-Oriented context suggestion

I). Multi-Label Classification (MLC)

We use LabelPowerset (LP) + RandomForest

II). Tensor Factorization (PITF)

Color, Shape, Weight, Origin,Taste, Price, Vitamin c

Users × Items × Contexts Ratings

Results and Findings

We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation.

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Simple Baseline By Context Rating Deviations By UI Context Suggestion

Results and Findings

We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation.

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Simple Baseline By Context Rating Deviations By UI Context Suggestion

Conclusions and Future Work

• Conclusions

PITF is the best algorithm; MLC is ranked the 2nd

CRD-based approach works better than baseline

• Future Work

Collect appropriate data & Perform user studies

Try other contextual recommendation algorithms

• Acknowledgement

Student Travel Grant by US NSF

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User-Oriented Context Suggestion

Yong Zheng, Bamshad Mobasher, Robin BurkeCenter for Web Intelligence

DePaul University, Chicago

The 24th Conference on User Modeling, Adaptation and PersonalizationHalifax, Canada, July 13-16, 2016