Query personalization : A short presentation done to identify quantitative & qualitative query personalization techniques

1. Query Personalization
(Subscriber Friendly)

2. Typical Pub/Sub
• All subscriptions are considered
equally
• Just matching a publication
whenever there’s a satisfied
subscription
Top-k Pub/Sub
• Users can express some events are
more important than others by
ranking subscriptions
• A publication is scored against a
satisfied subscription space
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&T
Item = Smartphone
Carrier = AT&T
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&T
Item = Smartphone
Carrier = AT&T

3. How a publication is covered by a subscription?
Let’s assume,
oSubscription (S) = {b1 Ʌ b2 Ʌ ……………….. Ʌ bq}
oPublication (P) = {a1 Ʌ a2 Ʌ ………………….Ʌ ap}
oP is covered by S, iff ϔbi ϵ S, then Ѐaj ϵ P
a1,a2,a3,………….....,ap
b1,b2,…………………,bq
Not covered
a1,a2,a3,………….....,ap
b1,b2,b3
b1,b2,b3,b4
b1,b2,……..bj
Covered!

4. Worst case scenario
• Bob subscribed to all matching subscriptions
Item = Smartphone
Item = Smartphone
Carrier = AT&T Carrier = AT&T
Item = Smartphone
Carrier = AT&T
OS = Android OS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
{φ}
|P| = n = 3
|S| = 2 𝑛 = 23
Item = Smartphone
Carrier = AT&T
OS = Android Subscription Space

5. How a subscription is covered by a subscription?
• Can be represented using a preference graph
• Given two subscriptions 𝑆𝑖 and 𝑆𝑗, 𝑆𝑖 covers 𝑆𝑗, iff,
• for each publication p
• s.t. 𝑆𝑗 covers p,
• it holds that 𝑆𝑖 covers p
• Node ≈ Subscription
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&TOS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone
Carrier = AT&T
OS = Android

6. How to assign preference over subscription?
Quantitative approach
• Assign interest to each
subscription
Qualitative approach
• Specify the interest between two
subscriptions
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&T
0.7
0.5
0.9
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&T
>
<

7. Interesting Question
• How can we compare quantitative & qualitative models, which are
used by a specific user?
• For the moment, Let’s go with quantitative approach

8. Worst case scenario
• Bob subscribed to all matching subscriptions
Item = Smartphone
Carrier = AT&T
OS = Android Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&TOS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone
Carrier = AT&T
OS = Android
0.7 0.5 0.9
0.8 0.6 0.9
0.8
Pref_score = Aggregation_op (score1,…….score8);
s.t. Aggregation_op ϵ {Max, Min, Average} Subscription Space

9. Preference graph performance
• Can prune useless subscriptions when walking along the graph for a
publication matching
• But in the worst case when nodes grow exponentially,
• It becomes bottleneck, when
• We have many users associated with each subscription
• The subscriptions are supported by many operators
• Attr {=,!=,>,<,…etc.} value
• Proposed solution
• Reduce the size of subscription space!

10. So How? (Open to discuss)
• We stick with the most specific subscription for a particular user that
can cover most number of other subscriptions
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&TOS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone
Carrier = AT&T
OS = Android
Subscription Space

11. So How? (Open to discuss)
• Instead of assign a score to the whole subscription, we assign a
comparison score to each attribute-value tuple
Bob
Item = Smartphone (0.4)
Carrier = AT&T (0.4)
OS = Android (0.2)
Subscription Space

12. How to assign comparison scores?
• Static way
• When user assigns scores, we
keep them as finalized score for
the subscription
• Dynamic way
• When user assigns scores, we
change them based on his
previous score assignment

13. Static assignment (On user demand)
Item = Smartphone (0.7)
Item = Smartphone (0.4)
Carrier = AT&T (0.4)
Item = Smartphone (0.4)
OS = Android (0.2)
Subscription Space

14. Dynamic assignment (Statistical model)
Item = Smartphone (0.7)
Item = Smartphone (0.4)
(operation[0.7, 0.4])
Carrier = AT&T (0.4)
Item = Smartphone (0.3)
(operation[0.7, 0.3])
OS = Android (0.3)
Subscription Space

15. Goal: In worst case
• Minimum number of most specific subscriptions can represent all
others, based on tuples with assigned scores
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&TOS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone (0.4)
Carrier = AT&T (0.3)
OS = Android (0.1)
Subscription Space

16. But what about the publications’ cover relation?
Let’s recap,
oSubscription (S) = {b1 Ʌ b2 Ʌ ……………….. Ʌ bq}
oPublication (P) = {a1 Ʌ a2 Ʌ ………………….Ʌ ap}
oP is covered by S, iff ϔbi ϵ S, then Ѐaj ϵ P
a1,a2,a3,………….....,ap
b1,b2,…………………,bq
Not covered!
a1,a2,a3,………….....,ap
b1,b2,b3
b1,b2,b3,b4
b1,b2,……..bj
Covered!

17. Worst case scenario
• Bob subscribed to all matching subscriptions
Item = Smartphone
Item = Smartphone
Carrier = AT&T Carrier = AT&T
Item = Smartphone
Carrier = AT&T
OS = Android OS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
{φ}
|P| = n = 3
|S| = 2 𝑛 = 23
Item = Smartphone
Carrier = AT&T
OS = Android
Subscription Space

18. Let’s change it a bit
• Recap!
oSubscription (S) = {b1 Ʌ b2 Ʌ ……………….. Ʌ bq}
oPublication (P) = {a1 Ʌ a2 Ʌ ………………….Ʌ ap}
oP is covered by S, iff at least Ѐbi ϵ S, then Ѐaj ϵ P
a1,a2,a3,………….....,ap
b1,b2,…………………,bq
Covered!
a1,a2,a3,………….....,ap
b1,b2,b3
b1,b2,b3,b4
b1,b2,……..bj
Covered!

19. Worst case scenario
• Now Bob’s single subscription is open for all partial matching
publications
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&T
OS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone
Carrier = AT&T
OS = Android….
Publication Space
Item = Smartphone
Item = Smartphone
Carrier = AT&T
Carrier = AT&TOS = Android
OS = Android
Carrier = AT&T
Item = Smartphone
OS = Android
Item = Smartphone (0.4)
Carrier = AT&T (0.3)
OS = Android (0.1)
Subscription Space

20. Correctness
• Our score assignment to the subscription tuples
• Does the trick?
• Should look out when applying other metrics too
• Publications’ diversification
• Minimize redundancy
• Source authority
• Reliable publication sources; Ex. Top seller
• Freshness
• Event windows
• To increase the novelty of delivered publications

21. REFERENCES
1) M. Drosou, E. Pitoura, and K. Stefanidis, “Preferential Publish /
Subscribe,” in Personalized Access, Profile Management, and
Context Awareness: Databases, 2008, pp. 9–16.
2) M. Drosou, K. Stefanidis, and E. Pitoura, “Preference-aware
publish/subscribe delivery with diversity,” Proc. Third ACM Int. Conf.
Distrib. Event-Based Syst. - DEBS ’09, p. 1, 2009.
3) M. Drosou, “Ranked Publish / Subscribe Delivery Extended abstract
for DEBS PhD Workshop,” PhD Work. conjunction with DEBS 2009
Conf., 2009.