In this exercise, firstly I have to evaluate the performance of different search engines.
Primarily, I have to choose two search engines I am familiar with such as Yahoo, Google, Bing!.
Second, I need to choose a target among the groups and design two queries to search in both search engines.
The target is chosen by the last number of your student ID. For example, if your student ID ends with the number is 1, please choose target 1; if it is 0, please choose target 10.
Thirdly, select the first 20 results in both search engines, if they return the target, then mark them as relevant documents, otherwise, they are irrelevant. The following exercises are based on your search results.
SOLUTION:
Search Engines: Google/Bing
Query 1 on Google search engine: Price+New+Macbook
Precision versus recall curves for Query 1 and interpolated to the 11 standard recall levels
Actual Recall & Precision Table |
Interpolated Recall & Precision Table |
||||||
Rank |
Relevant |
N |
Precision |
Recall |
Precision |
Recall |
|
1 |
R |
1 |
1.00 |
0.10 |
1 |
0.0 |
|
2 |
R |
2 |
1.00 |
0.20 |
1 |
0.1 |
|
3 |
2 |
0.67 |
0.20 |
1 |
0.2 |
||
4 |
2 |
0.50 |
0.20 |
0.71 |
0.3 |
||
5 |
R |
3 |
0.60 |
0.30 |
0.71 |
0.4 |
|
6 |
R |
4 |
0.67 |
0.40 |
0.71 |
0.5 |
|
7 |
R |
5 |
0.71 |
0.50 |
0.67 |
0.6 |
|
8 |
5 |
0.63 |
0.50 |
0.64 |
0.7 |
||
9 |
R |
6 |
0.67 |
0.60 |
0.64 |
0.8 |
|
10 |
6 |
0.60 |
0.60 |
0.64 |
0.9 |
||
11 |
R |
7 |
0.64 |
0.70 |
0.56 |
1.0 |
|
12 |
7 |
0.58 |
0.70 |
||||
13 |
R |
8 |
0.62 |
0.80 |
|||
14 |
R |
8 |
0.64 |
0.80 |
|||
15 |
8 |
0.60 |
0.80 |
||||
16 |
8 |
0.56 |
0.80 |
||||
17 |
8 |
0.53 |
0.80 |
||||
18 |
R |
9 |
0.56 |
0.90 |
|||
19 |
9 |
0.53 |
0.90 |
||||
20 |
9 |
0.50 |
0.90 |
Query 2 on Google search engine: MacBook + Price
Precision versus recall curves for Query 2 andinterpolated to the 11 standard recall levels
Actual Recall & Precision Table |
Interpolated Recall & Precision Table: |
|||||
Rank |
Relevant |
N |
Precision |
Recall |
Precision |
Recall |
1 |
R |
1 |
1.00 |
0.08 |
1 |
0.0 |
2 |
1 |
0.50 |
0.08 |
0.82 |
0.1 |
|
3 |
1 |
0.33 |
0.08 |
0.82 |
0.2 |
|
4 |
R |
2 |
0.50 |
0.17 |
0.82 |
0.3 |
5 |
R |
3 |
0.60 |
0.25 |
0.82 |
0.4 |
6 |
R |
4 |
0.67 |
0.33 |
0.82 |
0.5 |
7 |
R |
5 |
0.71 |
0.42 |
0.82 |
0.6 |
8 |
R |
6 |
0.75 |
0.50 |
0.82 |
0.7 |
9 |
R |
7 |
0.78 |
0.58 |
0.77 |
0.8 |
10 |
R |
8 |
0.80 |
0.67 |
0.69 |
0.9 |
11 |
R |
9 |
0.82 |
0.75 |
0.6 |
1.0 |
12 |
9 |
0.75 |
0.75 |
|||
13 |
R |
10 |
0.77 |
0.83 |
||
14 |
10 |
0.71 |
0.83 |
|||
15 |
10 |
0.67 |
0.83 |
|||
16 |
R |
11 |
0.69 |
0.92 |
||
17 |
11 |
0.65 |
0.92 |
|||
18 |
11 |
0.61 |
0.92 |
|||
19 |
11 |
0.58 |
0.92 |
|||
20 |
R |
12 |
0.60 |
1.00 |
Average on Google search engine
Interpolated Recall & Precision Table: |
||||
Precision |
Recall |
Precision Query 1 |
Precision Query 2 |
Average Precision |
1 |
0.0 |
1 |
1 |
1 |
0.82 |
0.1 |
1 |
0.82 |
0.91 |
0.82 |
0.2 |
1 |
0.82 |
0.91 |
0.82 |
0.3 |
0.71 |
0.82 |
0.77 |
0.82 |
0.4 |
0.71 |
0.82 |
0.77 |
0.82 |
0.5 |
0.71 |
0.82 |
0.77 |
0.82 |
0.6 |
0.67 |
0.82 |
0.75 |
0.82 |
0.7 |
0.64 |
0.82 |
0.73 |
0.77 |
0.8 |
0.64 |
0.77 |
0.71 |
0.69 |
0.9 |
0.64 |
0.69 |
0.67 |
0.6 |
1.0 |
0.56 |
0.6 |
0.58 |
Average precision versus recall curve for Search Engine 1 i.e. Google Search engine
Query 1 on BING search engine: Price New Macbook
Precision versus recall curves for Query 1 andinterpolated to the 11 standard recall levels
Actual Recall & Precision Table |
Interpolated Recall & Precision Table: |
|||||
Rank |
Relevant |
# |
Precision |
Recall |
Precision |
Recall |
1 |
R |
1 |
1.00 |
0.11 |
1 |
0.0 |
2 |
1 |
0.50 |
0.11 |
1 |
0.1 |
|
3 |
R |
2 |
0.67 |
0.22 |
0.75 |
0.2 |
4 |
R |
3 |
0.75 |
0.33 |
0.75 |
0.3 |
5 |
3 |
0.60 |
0.33 |
0.57 |
0.4 |
|
6 |
3 |
0.50 |
0.33 |
0.56 |
0.5 |
|
7 |
R |
4 |
0.57 |
0.44 |
0.5 |
0.6 |
8 |
4 |
0.50 |
0.44 |
0.5 |
0.7 |
|
9 |
R |
5 |
0.56 |
0.56 |
0.5 |
0.8 |
10 |
5 |
0.50 |
0.56 |
0.5 |
0.9 |
|
11 |
5 |
0.45 |
0.56 |
0.5 |
1.0 |
|
12 |
5 |
0.42 |
0.56 |
|||
13 |
R |
6 |
0.46 |
0.67 |
||
14 |
6 |
0.43 |
0.67 |
|||
15 |
R |
7 |
0.47 |
0.78 |
||
16 |
7 |
0.44 |
0.78 |
|||
17 |
R |
8 |
0.47 |
0.89 |
||
18 |
R |
9 |
0.50 |
1.00 |
||
19 |
9 |
0.47 |
1.00 |
|||
20 |
9 |
0.45 |
1.00 |
Query 2 on BING search engine: Macbook Price
Precision versus recall curves for Query 2 andinterpolated to the 11 standard recall levels
Actual Recall & Precision Table |
Interpolated Recall & Precision Table: |
|||||
Rank |
Relevant |
N |
Precision |
Recall |
Precision |
Recall |
1 |
R |
1 |
1.00 |
0.08 |
1 |
0.0 |
2 |
1 |
0.50 |
0.08 |
0.67 |
0.1 |
|
3 |
1 |
0.33 |
0.08 |
0.67 |
0.2 |
|
4 |
1 |
0.25 |
0.08 |
0.67 |
0.3 |
|
5 |
1 |
0.20 |
0.08 |
0.67 |
0.4 |
|
6 |
R |
2 |
0.33 |
0.17 |
0.67 |
0.5 |
7 |
R |
3 |
0.43 |
0.25 |
0.67 |
0.6 |
8 |
R |
4 |
0.50 |
0.33 |
0.67 |
0.7 |
9 |
R |
5 |
0.56 |
0.42 |
0.67 |
0.8 |
10 |
R |
6 |
0.60 |
0.50 |
0.63 |
0.9 |
11 |
R |
7 |
0.64 |
0.58 |
0.63 |
1.0 |
12 |
7 |
0.58 |
0.58 |
|||
13 |
R |
8 |
0.62 |
0.67 |
||
14 |
R |
9 |
0.64 |
0.75 |
||
15 |
R |
10 |
0.67 |
0.83 |
||
16 |
10 |
0.63 |
0.83 |
|||
17 |
10 |
0.59 |
0.83 |
|||
18 |
R |
11 |
0.61 |
0.92 |
||
19 |
R |
12 |
0.63 |
1.00 |
||
20 |
12 |
0.60 |
1.00 |
Average on Bing search engine
Average precision versus recall curve for Search Engine 2 i.e. Bing search engine
Interpolated Recall & Precision Table: |
||||
Precision |
Recall |
Precision Query 1 |
Precision Query 2 |
Average Precision |
1 |
0.0 |
1 |
1 |
1 |
0.82 |
0.1 |
1 |
0.67 |
0.84 |
0.82 |
0.2 |
0.75 |
0.67 |
0.71 |
0.82 |
0.3 |
0.75 |
0.67 |
0.71 |
0.82 |
0.4 |
0.57 |
0.67 |
0.62 |
0.82 |
0.5 |
0.56 |
0.67 |
0.62 |
0.82 |
0.6 |
0.5 |
0.67 |
0.59 |
0.82 |
0.7 |
0.5 |
0.67 |
0.59 |
0.77 |
0.8 |
0.5 |
0.67 |
0.59 |
0.69 |
0.9 |
0.5 |
0.63 |
0.57 |
0.6 |
1.0 |
0.5 |
0.63 |
0.57 |
Urgenthomework helped me with finance homework problems and taught math portion of my course as well. Initially, I used a tutor that taught me math course I felt that as if I was not getting the help I needed. With the help of Urgenthomework, I got precisely where I was weak: Sheryl. Read More
Follow Us