1. A novel Web usage mining approach for search engines
Authors: Dell Zhang, Yisheng Dong
Source: Computer Networks, Vol. 39, Issue 3, June , pp
Speaker: Pei-Yu Lin
Data: 8-May-03

2. User will only look an extremely small part of the search results only
Search engines locate information based on the textual similarity of a query
The search engine returns thousands of Web resource pointers to user after a general query
User will only look an extremely small part of the search results only
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3. Exploit the relationships among users, queries and resources
The output of the query is a list consisting of the resources with the highest quality weights(authority & freshness)

4. MASEL(matrix analysis on search engine log)
For the set of all users who have issued the query q* are constructed
All resources relevant to these queries can be constructed through traditional keyword-base IR.
Compute the numerical quality estimates of the found resources
Web resources with the highest quality weights are returned in order for the search topic

5. Ex: ‘Car’
time-window’s width = week
User Timestamp Query Results ：
Tom ‘Car’ img1, img2, img7, img4, img5, …
Tom ‘Auto’ img9, img3, img10, …
Tom ‘Bus’ img3, img6, img17, img13, …
Jack ‘Car’ img7, img1, img2, img4, img9, img6, …
Jack ‘Bus’ img1, img5, img4, img9, img2, …
Rose ‘Car’ img3, img1, img10, img9, img1, img6, …
Rose ‘Car’ img10, img1, img12, img14, img9, img6, …
Rose ‘Auto’ img14, img5, img3, img4, img9, img6, …

6. User
Timestamp
Query
Accessed images
Tom
970813
Car
img1, img1, img2
970817
Auto
970818
Bus
Jack
970814
img1, img2
img4
Rose
img1
970815
img3 DB

7. A = num(ui, qj) B = sim(qj, rk) C = hitq(rk, ui) User Timestamp Query
Accessed images
Tom
970813
Car
img1, img1, img2
970817
Auto
970818
Bus
Jack
970814
img1, img2
img4
Rose
img1
970815
img3
A = num(ui, qj) u q
B = sim(qj, rk) q r
C = hitq(rk, ui) 1 u r

8. return the well-ordered list of image: Rose, Tom, Jack
(1) u ← ABC u q q r 1 u r
Tom Jack Rose
r = (0.41, 0.4, 0.69)T
return the well-ordered list of image: Rose, Tom, Jack

9. return the well-ordered list of image: Car, Auto, Bus
(2) q ← BCA q r 1 u r u q
Car Auto Bus
r = (0.87, 0.14, 0.09)T
return the well-ordered list of image: Car, Auto, Bus

10. return the well-ordered list of image: img1, img2, img3, img4
(3) r ← CAB 1 u r u q q r
img1 img2 img3 img4
r = (0.96, 0.28, 0.03, 0.01)T
return the well-ordered list of image: img1, img2, img3, img4

11. Application in MASEL(eeFind)
Side effect in MASEL
Return some images labeled with ‘BMW’, ‘Porsche’ or ‘Rolls Royse’… because they are often queried by the uses with similar interests recently
query ‘Car’

12. Conclusions
The algorithm, MASEL, can exploit the relationships among users, queries and resources
The proposed approach reveals its power to achieve better ranking and query expansion effects