A Survey of Collaborative Filtering Techniques

Table 5

Content-boosted CF and its variations (a) content data and originally sparse rating data (b) pseudorating data filled by content predictor (c) predictions from (weighted) Pearson CF on the pseudo rating data.

(a)

	Content information				Rating matrix
	Age	Sex	Career	zip	I1	I2	I3	I4	I5
U1	32	F	writer	22904			4
U2	27	M	student	10022	2		4	3
U3	24	M	engineer	60402		1
U4	50	F	other	60804		3	3	3	3
U5	28	M	educator	85251	1

(b)

Pseudo rating data
I1	I2	I3	I4	I5
2	3	4	3	2
2	2	4	3	2
3	1	3	4	3
3	3	3	3	3
1	2	4	1	2

(c)

Pearson-CF prediction
I1	I2	I3	I4	I5
2	3	4	2	3
3	4	2	2	3
3	3	2	3	3
3	3	3	3	3
1	3	1	2	2