This paper proposes a method called user-oriented context suggestion that suggests contexts to users based on their preferences. It aims to maximize user experience by recommending not just good items, but appropriate contexts for those items. Two algorithms are developed: one based on contextual rating deviations that identifies how a user's ratings change across contexts, and another that adapts techniques from item-oriented context suggestion. An evaluation on a music dataset finds the tensor factorization approach performs best, with the contextual rating deviations method also outperforming a simple baseline. Future work includes collecting better evaluation data and trying other contextual recommendation algorithms.

1. User-Oriented Context Suggestion
Yong Zheng, Bamshad Mobasher, Robin Burke
Center for Web Intelligence
DePaul University, Chicago
The 24th Conference on User Modeling, Adaptation and Personalization
Halifax, Canada, July 13-16, 2016

2. Question: Is it enough to have
appropriate/good
item recommendations?

3. Zoo Parks in San Diego, USA
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• San Diego Zoo • San Diego Zoo Safari Park

4. Zoo Parks in San Diego, USA
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5. Intro: Context Suggestion

6. 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

7. 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

8. 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

9. 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

10. 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.

12. 12
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

15. 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

18. 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 context
conditions can be obtained
by the average rating on
context condition by users
across 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 0.5
if 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

24. 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

25. 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.
25
Simple Baseline By Context Rating Deviations By UI Context Suggestion

26. 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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27. User-Oriented Context Suggestion
Yong Zheng, Bamshad Mobasher, Robin Burke
Center for Web Intelligence
DePaul University, Chicago
The 24th Conference on User Modeling, Adaptation and Personalization
Halifax, Canada, July 13-16, 2016