Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context – identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.

1. Differential Context
Relaxation for
Context-Aware
Travel
Recomendation
Yong Zheng, Robin Burke, Bamshad Mobasher
Center for Web Intelligence
DePaul University

2. Recommender System
 Any system that guides the user in a
personalized way to interesting or useful
objects in a large space of possible
options or that produces such objects as
output.
 Context-aware recommendation
 means that our definition of “useful”
includes contextual considerations

3. Normal Recommendation
Profile
Restaurant1 Rating1
Restaurant2 Rating2
... ...

4. Context-Aware
Recommendation
Profile
Restaurant1 Rating1 Context1
Restaurant2 Rating2 Context2

5. Approaches to CARS
 Filtering
 discard all options not appropriate to context
 (either before or after)
 Modeling
 build context into recommendation model
 Critical question
 What contextual features matter?
 The more context features we use
 The more options are filtered out
 The sparser the modeling space

6. Context as constraints
 We can view context-aware recommendation
as imposing additional constraints on
recommendation
 Options must be suitable to the context
 But we may be willing to relax these constraints to
find nearby options

7. Relaxation
etc.

8. Context matching
 Assume we have a set of contextual features c
 c = < f1, f2, f3, ... fn >
 Define a set of constraints, C
 Two contexts c and d match relative to
constraints C iff
 Each feature in c and d matches relative to the
corresponding constraint in C

9. Example: Hotel Ratings
 { trip type, days stayed, origin city, destination
city, month of departure }
 c1 = {business, 3, Los Angeles, Chicago, July}
 c2 = {business, 7, Seattle, Chicago, January}
 Should they match or not?

10. Matching with constraints
 Two contexts
 c1 = {business, 3, Los Angeles, Chicago, July}
 c2 = {business, 7, Seattle, Chicago, January}
 Cstrict = { (exact trip type), (exact duration), (exact origin),
(exact destination), (exact month) }
 no match
 Crelaxed = { (exact trip type), (any duration), (contained
time_zone origin), (exact destination), (any month) }
 now the two contexts match
 If we are predicting for a user in context c2,
 we would not use a rating with context c1, if we apply
constraint Cstrict
 we would use it, if we apply Crelaxed

11. Differential Context Relaxation
 The idea is to apply context to different
components of a recommendation algorithm
 Rather than applying it in a uniform way
 Example
 kNN collaborative recommendation via Resnick’s
algorithm
Pred(u , i )  ru 
 vN
wv  (rv ,i  rv )
w
vN
v

12. Component 1: Neighbors
 Original algorithm
 Select neighbors who have rated item i
 Context-aware
 given context c
 Select neighbors who have rated item i in context
matching c, relative to constraint C1
Pred(u, i, c)  r 
 vN
wv  (rv,i  rv )
u
w v
vN

13. Component 2: Peer Baseline
 Original algorithm
 Average over all of the ratings by a neighbor to
establish a baseline
 Context-aware
 given context C
 Average over only those ratings matching c given
constraint C2
Pred(u, i, c)  r 
 vN
wv  (rv,i  rv )
u
w v
vN

14. Component 3: User baseline
 Original algorithm
 Average over all of the target user’s ratings to
establish a baseline
 Context-aware
 given context C
 Average over the target users ratings given in
contexts that match c, relative to constraint C3
Pred(u, i, c)  r 
 vN
wv  (rv,i  rv )
u
w v
vN

15. Question
 How to choose C1, C2, C3 to make best use of the
context information
 In other words
 what is the optimum relaxation of the contextual
constraint
 applied differentially to each algorithm component?

16. Data set
 Tripadvisor
 Top 50 US cities
 2,562 users
 1,455 hotels
 9,251 ratings
 Fairly difficult recommendation task
 some work using “Trip Type” as a contextual variable

17. Context-linked features
 We decided to use user location and hotel location
as context features
 Strictly speaking
 demographic
 content
 However research in the travel domain (Klenosky
and Gitelson, 1998) shows these factors influence
user’s expectations
 different standards for a California hotel vs a Nevada
one
 behave like contextual features
 We call these “context-linked” features

18. Feature space
 trip type – solo, family, business, etc.
 origin city
 contained state
 contained time zone
 destination city
 contained state
 contained time zone
 Total of 32 possibilities

19. Optimization
 32 feature possibilities
 3 components
 323 = 32k possible constraint combinations
 But possible to eliminate some possibilities
 Example
 when averaging over a given user’s ratings
 user location is irrelevant
 will not filter anything out
 Able to shrink to < 400 combinations
 enough for exhaustive search

20. Results

21. Optimal constraints

22. Sensitivity

23. Differential Context
Relaxation
 Lets us incorporate context
 While managing the tradeoff between accuracy and coverage
 Future considerations
 other algorithms
 F1 optimization constraint
 instead of binary matching, real-valued?
 instead of selection, weighting of features?
 scalable optimization
 Stay tuned!
 RecSys CARS workshop
Yong Zheng, Robin Burke, Bamshad Mobasher. "Optimal Feature Selection for Context-Aware
Recommendation using Differential Relaxation". Proceedings of the 4th International Workshop
on Context-Aware Recommender Systems (CARS 2012) held in conjunction with the 6th ACM
Conference on Recommender Systems (RecSys 2012), Dublin, Ireland, Sep 2012

24. Questions