Processing & Properties of Floor and Wall Tiles.pptx
[UMAP 2016] User-Oriented Context Suggestion
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
3
• San Diego Zoo • San Diego Zoo Safari Park
6. Traditional Recommender Systems
6
• 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
7
• 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
8
• 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?
9
• 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
11. 11
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!
17. 17
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
19. 19
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!
20. 20
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.
21. 21
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:
23. 23
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.
24
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
26
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