TopicAsContext considering the Context of Items

In this case the YelpCleanNormal dataset was also not considered, since text normaliza-tion did not improve the results considerably. In the same way as in the EntityAsCon-text method, considering conEntityAsCon-texts by the TopicAsConEntityAsCon-text method as ConEntityAsCon-text of Items greatly improved the results, especially for the algorithms C. Reduction and Weight PoF

(Table 8).

In all cases the algorithms C. Reduction and Weight PoF were better or equivalent to IBCF. Regarding Filter PoF, its results were lower than the IBCF results, with statistical signicance, in all cases.

Table 8: Comparing the context-aware recommendation algorithms using contexts of the method TopicAsContext against the non-contextual baseline IBCF. The values that are statistically dierent than IBCF (p-value>0.05) are together with a asterisk and the values that are better than IBCF are in boldface (considering the Context of Items).

Context IBCF YelpClean

C. Reduc. Weight PoF Filter PoF Date 0 2 7 0.0215 0.02300.02300.0230 0.02640.02640.0264∗∗∗ 0.0033∗ Date 0 10 50 0.0215 0.02190.02190.0219 0.02280.02280.0228 0.0051∗ Date 0 15 20 0.0215 0.02720.02720.0272∗∗∗ 0.02960.02960.0296∗∗∗ 0.0041∗ Date 0 50 100 0.0215 0.02190.02190.0219 0.02270.02270.0227 0.0043∗ Date 05 2 7 0.0215 0.02380.02380.0238∗∗∗ 0.02720.02720.0272∗∗∗ 0.0025∗ Date 05 10 50 0.0215 0.02180.02180.0218 0.02300.02300.0230 0.0045∗ Date 05 15 20 0.0215 0.02610.02610.0261∗∗∗ 0.02820.02820.0282∗∗∗ 0.0019∗ Date 05 50 100 0.0215 0.02240.02240.0224 0.02300.02300.0230 0.0042∗ Date 1 2 7 0.0215 0.02210.02210.0221 0.02510.02510.0251∗∗∗ 0.0014∗ Date 1 10 50 0.0215 0.02380.02380.0238 0.02500.02500.0250∗∗∗ 0.0050∗ Date 1 15 20 0.0215 0.02500.02500.0250∗∗∗ 0.02700.02700.0270∗∗∗ 0.0067∗ Date 1 50 100 0.0215 0.02330.02330.0233 0.02390.02390.0239∗∗∗ 0.0043∗ DateLocal 0 2 7 0.0215 0.02380.02380.0238 0.02770.02770.0277∗∗∗ 0.0089∗ DateLocal 0 10 50 0.0215 0.02240.02240.0224 0.02350.02350.0235 0.0043∗ DateLocal 0 15 20 0.0215 0.02530.02530.0253∗∗∗ 0.02760.02760.0276∗∗∗ 0.0055∗ DateLocal 0 50 100 0.0215 0.02270.02270.0227 0.02310.02310.0231 0.0028∗ DateLocal 05 2 7 0.0215 0.02340.02340.0234 0.02690.02690.0269∗∗∗ 0.0044∗ DateLocal 05 10 50 0.0215 0.0214 0.02240.02240.0224 0.0005∗ DateLocal 05 15 20 0.0215 0.02610.02610.0261∗∗∗ 0.02790.02790.0279∗∗∗ 0.0094∗ DateLocal 05 50 100 0.0215 0.02230.02230.0223 0.02290.02290.0229 0.0037∗ DateLocal 1 2 7 0.0215 0.02230.02230.0223 0.02510.02510.0251∗∗∗ 0.0036∗ DateLocal 1 10 50 0.0215 0.02170.02170.0217 0.02250.02250.0225 0.0083∗ DateLocal 1 15 20 0.0215 0.02510.02510.0251∗∗∗ 0.02670.02670.0267∗∗∗ 0.0061∗ DateLocal 1 50 100 0.0215 0.02310.02310.0231 0.02390.02390.0239 0.0014∗ DateLocalOrg 0 2 7 0.0215 0.02260.02260.0226 0.02640.02640.0264∗∗∗ 0.0049∗ DateLocalOrg 0 10 50 0.0215 0.02280.02280.0228 0.02360.02360.0236 0.0045∗ DateLocalOrg 0 15 20 0.0215 0.02630.02630.0263∗∗∗ 0.02930.02930.0293∗∗∗ 0.0054∗ DateLocalOrg 0 50 100 0.0215 0.0205 0.0212 0.0007∗ DateLocalOrg 05 2 7 0.0215 0.02530.02530.0253∗∗∗ 0.02890.02890.0289∗∗∗ 0.0106∗ DateLocalOrg 05 10 50 0.0215 0.02340.02340.0234∗∗∗ 0.02450.02450.0245∗∗∗ 0.0057∗ DateLocalOrg 05 15 20 0.0215 0.02790.02790.0279∗∗∗ 0.03060.03060.0306∗∗∗ 0.0086∗ DateLocalOrg 05 50 100 0.0215 0.02230.02230.0223 0.02290.02290.0229 0.0037∗ DateLocalOrg 1 2 7 0.0215 0.02450.02450.0245∗∗∗ 0.02800.02800.0280∗∗∗ 0.0081∗ DateLocalOrg 1 10 50 0.0215 0.02360.02360.0236 0.02460.02460.0246∗∗∗ 0.0064∗ DateLocalOrg 1 15 20 0.0215 0.02840.02840.0284∗∗∗ 0.03100.03100.0310∗∗∗ 0.0087∗ DateLocalOrg 1 50 100 0.0215 0.02180.02180.0218 0.02280.02280.0228 0.0025∗ DateLocalTime 0 2 7 0.0215 0.02330.02330.0233 0.02690.02690.0269∗∗∗ 0.0090∗ DateLocalTime 0 10 50 0.0215 0.02300.02300.0230 0.02380.02380.0238∗∗∗ 0.0043∗ DateLocalTime 0 15 20 0.0215 0.02670.02670.0267∗∗∗ 0.02900.02900.0290∗∗∗ 0.0081∗

Table 8: (continued)

Context IBCF YelpClean

C. Reduc. Weight PoF Filter PoF DateLocalTime 0 50 100 0.0215 0.02260.02260.0226 0.02330.02330.0233 0.0037∗

DateLocalTime 05 2 7 0.0215 0.02300.02300.0230 0.02630.02630.0263∗∗∗ 0.0085∗ DateLocalTime 05 10 50 0.0215 0.0213 0.02230.02230.0223 0.00220.00220.0022∗∗∗ DateLocalTime 05 15 20 0.0215 0.02440.02440.0244 0.02670.02670.0267∗∗∗ 0.0046∗ DateLocalTime 05 50 100 0.0215 0.0161 0.02300.02300.0230 0.0052∗ DateLocalTime 1 2 7 0.0215 0.02420.02420.0242 0.02730.02730.0273∗∗∗ 0.0073∗ DateLocalTime 1 10 50 0.0215 0.02170.02170.0217 0.02250.02250.0225 0.0057∗ DateLocalTime 1 15 20 0.0215 0.02430.02430.0243 0.02590.02590.0259∗∗∗ 0.0098∗ DateLocalTime 1 50 100 0.0215 0.02350.02350.0235 0.02430.02430.0243∗∗∗ 0.0062∗ DateOrg 0 2 7 0.0215 0.02300.02300.0230 0.02680.02680.0268∗∗∗ 0.0060∗ DateOrg 0 10 50 0.0215 0.02230.02230.0223 0.02340.02340.0234 0.0036∗ DateOrg 0 15 20 0.0215 0.02870.02870.0287∗∗∗ 0.03100.03100.0310∗∗∗ 0.0096∗ DateOrg 0 50 100 0.0215 0.0210 0.02180.02180.0218 0.0034∗ DateOrg 05 2 7 0.0215 0.02520.02520.0252∗∗∗ 0.02980.02980.0298∗∗∗ 0.0077∗ DateOrg 05 10 50 0.0215 0.02220.02220.0222 0.02320.02320.0232 0.0041∗ DateOrg 05 15 20 0.0215 0.02760.02760.0276∗∗∗ 0.03030.03030.0303∗∗∗ 0.0103∗ DateOrg 05 50 100 0.0215 0.02240.02240.0224 0.02310.02310.0231 0.0009∗ DateOrg 1 2 7 0.0215 0.02530.02530.0253∗∗∗ 0.02840.02840.0284∗∗∗ 0.0113∗ DateOrg 1 10 50 0.0215 0.02360.02360.0236 0.02450.02450.0245∗∗∗ 0.0077∗ DateOrg 1 15 20 0.0215 0.02710.02710.0271∗∗∗ 0.02970.02970.0297∗∗∗ 0.0089∗ DateOrg 1 50 100 0.0215 0.02400.02400.0240∗∗∗ 0.02490.02490.0249∗∗∗ 0.0039∗ DateOrgTime 0 2 7 0.0215 0.02440.02440.0244∗∗∗ 0.02770.02770.0277∗∗∗ 0.0084∗ DateOrgTime 0 10 50 0.0215 0.02350.02350.0235∗∗∗ 0.02450.02450.0245∗∗∗ 0.0046∗ DateOrgTime 0 15 20 0.0215 0.02710.02710.0271∗∗∗ 0.02990.02990.0299∗∗∗ 0.0061∗ DateOrgTime 0 50 100 0.0215 0.02250.02250.0225 0.02300.02300.0230 0.0036∗ DateOrgTime 05 2 7 0.0215 0.02390.02390.0239∗∗∗ 0.02730.02730.0273∗∗∗ 0.0099∗ DateOrgTime 05 10 50 0.0215 0.0207 0.02160.02160.0216 0.0043 DateOrgTime 05 15 20 0.0215 0.02350.02350.0235 0.02580.02580.0258∗∗∗ 0.0039∗ DateOrgTime 05 50 100 0.0215 0.0212 0.02190.02190.0219 0.0043∗ DateOrgTime 1 2 7 0.0215 0.02310.02310.0231 0.02630.02630.0263∗∗∗ 0.0074∗ DateOrgTime 1 10 50 0.0215 0.02180.02180.0218 0.02280.02280.0228 0.0053∗ DateOrgTime 1 15 20 0.0215 0.02600.02600.0260∗∗∗ 0.02830.02830.0283∗∗∗ 0.0065∗ DateOrgTime 1 50 100 0.0215 0.0211 0.02210.02210.0221 0.0009∗ DateTime 0 2 7 0.0215 0.02350.02350.0235∗∗∗ 0.02670.02670.0267∗∗∗ 0.0091∗ DateTime 0 10 50 0.0215 0.02340.02340.0234∗∗∗ 0.02400.02400.0240∗∗∗ 0.0015∗ DateTime 0 15 20 0.0215 0.02650.02650.0265∗∗∗ 0.02870.02870.0287∗∗∗ 0.0082∗ DateTime 0 50 100 0.0215 0.02210.02210.0221 0.02280.02280.0228 0.0040∗ DateTime 05 2 7 0.0215 0.02390.02390.0239 0.02730.02730.0273∗∗∗ 0.0029∗ DateTime 05 10 50 0.0215 0.0212 0.02220.02220.0222 0.0012∗ DateTime 05 15 20 0.0215 0.02480.02480.0248 0.02670.02670.0267∗∗∗ 0.0036∗ DateTime 05 50 100 0.0215 0.02260.02260.0226 0.02350.02350.0235 0.0049∗ DateTime 1 2 7 0.0215 0.02330.02330.0233 0.02600.02600.0260∗∗∗ 0.0014∗ DateTime 1 10 50 0.0215 0.0209 0.02190.02190.0219 0.0008∗ DateTime 1 15 20 0.0215 0.02400.02400.0240∗∗∗ 0.02600.02600.0260∗∗∗ 0.0039∗ DateTime 1 50 100 0.0215 0.02160.02160.0216 0.02250.02250.0225 0.0043∗ Local 0 2 7 0.0215 0.02250.02250.0225 0.02600.02600.0260∗∗∗ 0.0092∗ Local 0 10 50 0.0215 0.02160.02160.0216 0.02300.02300.0230 0.0054∗ Local 0 15 20 0.0215 0.02500.02500.0250∗∗∗ 0.02760.02760.0276∗∗∗ 0.0094∗ Local 0 50 100 0.0215 0.02170.02170.0217 0.02240.02240.0224 0.0032∗ Local 05 2 7 0.0215 0.02310.02310.0231 0.02700.02700.0270∗∗∗ 0.0056∗

Table 8: (continued)

Context IBCF YelpClean

C. Reduc. Weight PoF Filter PoF Local 05 10 50 0.0215 0.02310.02310.0231 0.02420.02420.0242∗∗∗ 0.0044∗ Local 05 15 20 0.0215 0.02410.02410.0241 0.02620.02620.0262∗∗∗ 0.0042∗ Local 05 50 100 0.0215 0.02200.02200.0220 0.02260.02260.0226 0.0009∗ Local 1 2 7 0.0215 0.02290.02290.0229 0.02560.02560.0256∗∗∗ 0.0077∗ Local 1 10 50 0.0215 0.02440.02440.0244∗∗∗ 0.02560.02560.0256∗∗∗ 0.0085∗ Local 1 15 20 0.0215 0.02590.02590.0259∗∗∗ 0.02800.02800.0280∗∗∗ 0.0107∗ Local 1 50 100 0.0215 0.02190.02190.0219 0.02270.02270.0227 0.0052∗ LocalOrg 0 2 7 0.0215 0.02490.02490.0249∗∗∗ 0.02880.02880.0288∗∗∗ 0.0134∗ LocalOrg 0 10 50 0.0215 0.02360.02360.0236 0.02450.02450.0245∗∗∗ 0.0049∗ LocalOrg 0 15 20 0.0215 0.02610.02610.0261∗∗∗ 0.02890.02890.0289∗∗∗ 0.0078∗ LocalOrg 0 50 100 0.0215 0.02280.02280.0228 0.02390.02390.0239 0.0035∗ LocalOrg 05 2 7 0.0215 0.02350.02350.0235 0.02730.02730.0273∗∗∗ 0.0089∗ LocalOrg 05 10 50 0.0215 0.02270.02270.0227 0.02370.02370.0237 0.0034∗ LocalOrg 05 15 20 0.0215 0.02570.02570.0257∗∗∗ 0.02810.02810.0281∗∗∗ 0.0059∗ LocalOrg 05 50 100 0.0215 0.02260.02260.0226 0.02330.02330.0233∗∗∗ 0.0033∗ LocalOrg 1 2 7 0.0215 0.02380.02380.0238 0.02700.02700.0270∗∗∗ 0.0084∗ LocalOrg 1 10 50 0.0215 0.02300.02300.0230 0.02400.02400.0240 0.0090 LocalOrg 1 15 20 0.0215 0.02620.02620.0262∗∗∗ 0.02850.02850.0285∗∗∗ 0.0109∗ LocalOrg 1 50 100 0.0215 0.02340.02340.0234 0.02420.02420.0242 0.0044∗ LocalOrgTime 0 2 7 0.0215 0.02260.02260.0226 0.02620.02620.0262∗∗∗ 0.0089∗ LocalOrgTime 0 10 50 0.0215 0.02220.02220.0222 0.02310.02310.0231 0.0045∗ LocalOrgTime 0 15 20 0.0215 0.02590.02590.0259∗∗∗ 0.02860.02860.0286∗∗∗ 0.0104∗ LocalOrgTime 0 50 100 0.0215 0.02280.02280.0228 0.02340.02340.0234 0.0032∗ LocalOrgTime 05 2 7 0.0215 0.02190.02190.0219 0.02570.02570.0257∗∗∗ 0.0039∗ LocalOrgTime 05 10 50 0.0215 0.0215 0.02230.02230.0223 0.0043∗ LocalOrgTime 05 15 20 0.0215 0.02460.02460.0246∗∗∗ 0.02750.02750.0275∗∗∗ 0.0069∗ LocalOrgTime 05 50 100 0.0215 0.0214 0.02210.02210.0221 0.0011∗ LocalOrgTime 1 2 7 0.0215 0.02370.02370.0237 0.02670.02670.0267∗∗∗ 0.0076∗ LocalOrgTime 1 10 50 0.0215 0.02320.02320.0232 0.02420.02420.0242∗∗∗ 0.0059∗ LocalOrgTime 1 15 20 0.0215 0.02530.02530.0253∗∗∗ 0.02760.02760.0276∗∗∗ 0.0070∗ LocalOrgTime 1 50 100 0.0215 0.02340.02340.0234 0.02420.02420.0242 0.0044∗ LocalTime 0 2 7 0.0215 0.02240.02240.0224 0.02560.02560.0256∗∗∗ 0.0089∗ LocalTime 0 10 50 0.0215 0.02440.02440.0244∗∗∗ 0.02550.02550.0255∗∗∗ 0.0042∗ LocalTime 0 15 20 0.0215 0.02530.02530.0253∗∗∗ 0.02760.02760.0276∗∗∗ 0.0051∗ LocalTime 0 50 100 0.0215 0.02360.02360.0236 0.02430.02430.0243 0.0042∗ LocalTime 05 2 7 0.0215 0.0205 0.02400.02400.0240∗∗∗ 0.0085∗ LocalTime 05 10 50 0.0215 0.02170.02170.0217 0.02280.02280.0228 0.0041∗ LocalTime 05 15 20 0.0215 0.02460.02460.0246 0.02750.02750.0275∗∗∗ 0.0087∗ LocalTime 05 50 100 0.0215 0.02170.02170.0217 0.02270.02270.0227 0.0048∗ LocalTime 1 2 7 0.0215 0.02350.02350.0235∗∗∗ 0.02660.02660.0266∗∗∗ 0.0079∗ LocalTime 1 10 50 0.0215 0.02310.02310.0231∗∗∗ 0.02370.02370.0237∗∗∗ 0.0077∗ LocalTime 1 15 20 0.0215 0.02390.02390.0239∗∗∗ 0.02560.02560.0256∗∗∗ 0.0029∗ LocalTime 1 50 100 0.0215 0.02270.02270.0227∗∗∗ 0.02340.02340.0234∗∗∗ 0.0058∗ Org 0 2 7 0.0215 0.02450.02450.0245∗∗∗ 0.02760.02760.0276∗∗∗ 0.0112∗ Org 0 10 50 0.0215 0.02170.02170.0217 0.02260.02260.0226 0.0041∗ Org 0 15 20 0.0215 0.02730.02730.0273∗∗∗ 0.02960.02960.0296∗∗∗ 0.0093∗ Org 0 50 100 0.0215 0.02220.02220.0222 0.02310.02310.0231 0.0030∗ Org 05 2 7 0.0215 0.02330.02330.0233 0.02760.02760.0276∗∗∗ 0.0115∗ Org 05 10 50 0.0215 0.02310.02310.0231 0.02430.02430.0243 0.0055∗ Org 05 15 20 0.0215 0.02850.02850.0285∗∗∗ 0.03210.03210.0321∗∗∗ 0.0079∗

Table 8: (continued)

Context IBCF YelpClean

C. Reduc. Weight PoF Filter PoF Org 05 50 100 0.0215 0.02170.02170.0217 0.02260.02260.0226 0.0006∗

Org 1 2 7 0.0215 0.02420.02420.0242∗∗∗ 0.02850.02850.0285∗∗∗ 0.0121∗ Org 1 10 50 0.0215 0.02220.02220.0222 0.02310.02310.0231∗∗∗ 0.0007∗ Org 1 15 20 0.0215 0.02620.02620.0262∗∗∗ 0.02890.02890.0289∗∗∗ 0.0037∗ Org 1 50 100 0.0215 0.02200.02200.0220 0.02280.02280.0228 0.0011∗ OrgTime 0 2 7 0.0215 0.02470.02470.0247 0.02770.02770.0277∗∗∗ 0.0104∗ OrgTime 0 10 50 0.0215 0.02230.02230.0223 0.02340.02340.0234 0.0047∗ OrgTime 0 15 20 0.0215 0.02860.02860.0286∗∗∗ 0.03140.03140.0314∗∗∗ 0.0037∗ OrgTime 0 50 100 0.0215 0.02300.02300.0230 0.02380.02380.0238 0.0037∗ OrgTime 05 2 7 0.0215 0.02320.02320.0232 0.02750.02750.0275∗∗∗ 0.0049∗ OrgTime 05 10 50 0.0215 0.02260.02260.0226 0.02370.02370.0237 0.0028∗ OrgTime 05 15 20 0.0215 0.02770.02770.0277∗∗∗ 0.03080.03080.0308∗∗∗ 0.0089∗ OrgTime 05 50 100 0.0215 0.02220.02220.0222 0.02320.02320.0232 0.0034∗ OrgTime 1 2 7 0.0215 0.02480.02480.0248∗∗∗ 0.02820.02820.0282∗∗∗ 0.0109∗ OrgTime 1 10 50 0.0215 0.02390.02390.0239 0.02490.02490.0249 0.0063∗ OrgTime 1 15 20 0.0215 0.02400.02400.0240 0.02650.02650.0265∗∗∗ 0.0020∗ OrgTime 1 50 100 0.0215 0.02330.02330.0233 0.02410.02410.0241 0.0011∗ Time 0 2 7 0.0215 0.02300.02300.0230 0.02640.02640.0264∗∗∗ 0.0072∗ Time 0 10 50 0.0215 0.02190.02190.0219 0.02270.02270.0227 0.0045∗ Time 0 15 20 0.0215 0.02580.02580.0258∗∗∗ 0.02840.02840.0284∗∗∗ 0.0029∗ Time 0 50 100 0.0215 0.02170.02170.0217 0.02220.02220.0222 0.0039∗ Time 05 2 7 0.0215 0.02420.02420.0242∗∗∗ 0.02710.02710.0271∗∗∗ 0.0068∗ Time 05 10 50 0.0215 0.02250.02250.0225 0.02340.02340.0234 0.0042∗ Time 05 15 20 0.0215 0.02530.02530.0253∗∗∗ 0.02710.02710.0271∗∗∗ 0.0056∗ Time 05 50 100 0.0215 0.02170.02170.0217 0.02260.02260.0226 0.0048∗ Time 1 2 7 0.0215 0.0213 0.02370.02370.0237 0.0062∗ Time 1 10 50 0.0215 0.0211 0.02210.02210.0221 0.0055∗ Time 1 15 20 0.0215 0.02390.02390.0239 0.02580.02580.0258∗∗∗ 0.0060∗ Time 1 50 100 0.0215 0.02330.02330.0233 0.02390.02390.0239∗∗∗ 0.0043∗ DateLocalOrgTime 0 2 7 0.0215 0.02350.02350.0235∗∗∗ 0.02740.02740.0274∗∗∗ 0.0094∗ DateLocalOrgTime 0 10 50 0.0215 0.02290.02290.0229 0.02370.02370.0237 0.0040∗ DateLocalOrgTime 0 15 20 0.0215 0.03010.03010.0301∗∗∗ 0.03010.03010.0301∗∗∗ 0.0020∗ DateLocalOrgTime 0 50 100 0.0215 0.0213 0.02180.02180.0218 0.0033∗ DateLocalOrgTime 05 2 7 0.0215 0.02230.02230.0223 0.02320.02320.0232 0.0070∗ DateLocalOrgTime 05 10 50 0.0215 0.02370.02370.0237 0.02670.02670.0267∗∗∗ 0.0087∗ DateLocalOrgTime 05 15 20 0.0215 0.02240.02240.0224 0.02330.02330.0233 0.0049∗ DateLocalOrgTime 05 50 100 0.0215 0.02540.02540.0254∗∗∗ 0.02780.02780.0278∗∗∗ 0.0054∗ DateLocalOrgTime 1 2 7 0.0215 0.02410.02410.0241∗∗∗ 0.02660.02660.0266∗∗∗ 0.0077∗ DateLocalOrgTime 1 10 50 0.0215 0.02250.02250.0225 0.02350.02350.0235 0.0028∗ DateLocalOrgTime 1 15 20 0.0215 0.02710.02710.0271∗∗∗ 0.02960.02960.0296∗∗∗ 0.0039∗ DateLocalOrgTime 1 50 100 0.0215 0.02230.02230.0223 0.02320.02320.0232 0.0070∗

In Table 9, we present the best congurations for each privileged information. For the

al-gorithm C. Reduction, the best result was obtained using the context DateLocalOrgTime 0 15 20.

For the Weight PoF algorithm, the context Org 05 15 20 was the one that generated the

best result. For the algorithm Filter PoF, the best result was obtained using the context LocalOrg 0 2 7.

Table 9: The best congurations and values of M AP@10 (between parenthesis) for each privi-leged information, considering the method TopicAsContext and the Context of Items.

Privileged YelpClean

Information C. Weight Filter

Reduc. PoF PoF

Date 0 15 20 0 15 20 1 15 20 (0.0272) (0.0296) (0.0067) DateLocal 05 15 20 05 15 20 05 15 20 (0.0261) (0.0279) (0.0094) DateLocalOrg 1 15 20 1 15 20 05 2 7

(0.0284) (0.0310) (0.0106) DateLocalTime 0 15 20 0 15 20 1 15 20 (0.0267) (0.0290) (0.0098) DateOrg 0 15 20 0 15 20 1 2 7

(0.0287) (0.0310) (0.0113) DateOrgTime 0 15 20 0 15 20 05 2 7

(0.0271) (0.0299) (0.0099) DateTime 0 15 20 0 15 20 0 2 7

(0.0265) (0.0287) (0.0091) Local 1 15 20 1 15 20 1 15 20 (0.0259) (0.0280) (0.0107) LocalOrg 1 15 20 0 15 20 0 2 7

(0.0262) (0.0289) (0.0134)

LocalOrgTime 0 2 7 0 2 7 0 2 7

(0.0259) (0.0286) (0.0104) LocalTime 0 15 20 0 15 20 0 2 7

(0.0253) (0.0276) (0.0089)

Org 05 15 20 05 15 20 1 2 7

(0.0285) (0.0321) (0.0121) OrgTime 0 15 20 0 15 20 1 2 7

(0.0286) (0.0314) (0.0109)

Time 0 15 20 0 15 20 0 2 7

(0.0258) (0.0284) (0.0072) DateLocalOrgTime 0 15 20 0 15 20 0 2 7

(0.0301) (0.0301) (0.0094)

In Figure 6, some of the best results of the method TopicAsContext are presented in three graphs. The rst graph shows the results for the dataset YelpClean using the Context of Reviews. The second graph shows the results for the dataset YelpCleanNormal using the Context of Reviews. And nally, in the third we look at the results for the dataset Yelp-Clean using the Context of Items. Analyzing the graphs, we note that: the results for the dataset YelpClean were higher than the results of the dataset YelpCleanNormal and the results using the Context of Items were higher than the results when the Context of Reviews was used.

Figure 6: Graphics with some of the best results of the method TopicAsContext.

4. Final Remarks

Recommender systems are systems that recommend items that may be interesting to users. Traditional systems consider only item and user information to generate recom-mendations. However, the use of additional information, such as contextual information, may result in more accurate recommendations. In this sense there is a class of recom-mender systems, called context-aware recomrecom-mender systems, that generates recommen-dations using information from users and items, as well as contextual information.

Work in the area of context-aware recommender systems has shown that the use of the contextual information improves the accuracy of the recommendation. However, there is a diculty in extracting such information. There is a lack of automatic and eective extraction methods. In addition, it is necessary to dene the best source for extracting relevant contextual information.

With the advent of Web 2.0, users have generated a lot of their own content through reviews, posts on social networks, etc. Such content is rich in information about the user context and opinion. From the textual content of reviews, we can extract a lot of information that can be useful in the recommendation process.

Some works have already been developed with the intention of proposing methods for the contextual information extraction. Two of these methods were proposed and ap-plied in the Web page domain. One of them, the EntityAsContext method, proposed by Domingues et al. (2014), consists of extracting named entities from the textual content of web pages and using them as contextual information in recommender systems. The sec-ond method, TopicAsContext, proposed by Sundermann et al. (2016), consists of building topic hierarchies of web pages, extracting and using the topics as contextual information.

The objective of this work was to apply the two methods previously mentioned in the do-main of reviews and to evaluate the recommendations generated in this scenario, building, in this way, baselines for future works. For this purpose, the Yelp dataset made available for the ACM RecSysChallenge 2013 was used. The review texts were extracted, passed through a cleaning process and also normalized, generating the YelpClean and YelpClean-Normal datasets. The evaluation consisted of comparing the values of the MAP metric, obtained by four context-aware recommender systems, against the MAP values obtained by the IBCF method, that does not use context. The constextual information that was fed into the context-aware recommender systems was extracted by the EntityAsContext and TopicAsContext methods.

Results were presented and discussed, taking into account the two methods

(EntityAs-Context and TopicAs(EntityAs-Context), the datasets and the two ways of considering contex-tual information. The text normalization did not improve the quality of the contexts extracted, as there was no statistically signicant improvement in the performance of the recommendation. In addition, the Context of Items generated more precise rec-ommendations than the Context of Reviews. Finally, the EntityAsContext method outperformed the TopicAsContext method`.

Concluding, in this work we analyzed the performance of methods already proposed in the literature being applied in the domain of reviews. The results of the evaluation can be used in other works as baselines for new methods. In addition, this work, as well as the discussion of the results, can inspire and assist in other work in development.

This study was nanced in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES) Finance Code 001, Conselho Nacional de Desenvolvimento Cientíco e Tecnológico -Brasil (CNPq) - grant #403648/2016-5, and Fundação Araucária de Apoio ao Desenvolvimento Cientíco e Tecnológico do Estado do Paraná - Brasil (FAPPR).

References

Adomavicius, G., R. Sankaranarayanan, S. Sen, & A. Tuzhilin (2005). Incorporating contextual information in recommender systems using a multidimensional approach.

ACM Transactions on Information Systems 23 (1), 103145.

Adomavicius, G. & A. Tuzhilin (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (6), 734749.

Adomavicius, G. & A. Tuzhilin (2011). Context-aware recommender systems. In Recom-mender Systems Handbook, pp. 217253. Springer.

Bauman, K. & A. Tuzhilin (2014). Discovering contextual information from user reviews for recommendation purposes. In CBRecSys'14: Proceedings of Workshop on New Trends in Content-based Recommender Systems, pp. 29.

Biancalana, C., F. Gasparetti, A. Micarelli, & G. Sansonetti (2013, February). An ap-proach to social recommendation for context-aware mobile services. ACM Transactions on Intelligent Systems and Technology 4 (1), 10:110:31.

Breese, J. S., D. Heckerman, & C. Kadie (1998). Empirical analysis of predictive algo-rithms for collaborative ltering. In UAI'98: Proceedings of the Fourteenth Conference on Uncertainty in Articial Intelligence, pp. 4352.

Cardoso, N. (2008). Rembrandt - reconhecimento de entidades mencionadas baseado em relaç oes e análise detalhada do texto. In Desaos na avaliação conjunta do reconhec-imento de entidades mencionadas: O Segundo HAREM, pp. 195211. Linguateca.

Chen, G. & L. Chen (2014). Recommendation based on contextual opinions. In User Modeling, Adaptation, and Personalization, Volume 8538 of Lecture Notes in Computer Science, pp. 6173. Springer International Publishing.

Chen, G. & L. Chen (2015). Augmenting service recommender systems by incorporat-ing contextual opinions from user reviews. User Modelincorporat-ing and User-Adapted Interac-tion 25 (3), 295329.

Conrado, M. S. (2014). Extração automática de termos simples baseada em aprendizado de máquina. Tese de Doutorado, Instituto de Ciências Matemáticas e de Computação - Universidade de São Paulo - USP.

Conrado, M. S., T. A. S. Pardo, & S. O. Rezende (2013). A machine learning approach to automatic term extraction using a rich feature set. In NAACL-HLT-SRW'13: Pro-ceedings of the Conference of the North American Chapter of the Association for Com-putational Linguistics: Human Language Technologies - Student Research Workshop, Atlanta, Georgia, pp. 1623. Association for Computational Linguistics.

Cunningham, H., D. Maynard, K. Bontcheva, & V. Tablan (2002). Gate: an architecture for development of robust hlt applications. In ACL'02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, pp.

168175. Association for Computational Linguistics.

Deshpande, M. & G. Karypis (2004). Item-based top-n recommendation algorithms.

ACM Transactions on Information Systems 22 (1), 143177.

Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Comput-ing 5 (1), 47.

Domingues, M. A., A. M. Jorge, & C. Soares (2013). Dimensions as virtual items:

Improving the predictive ability of top-n recommender systems. Information Processing and Management: an International Journal 49 (3), 698720.

Domingues, M. A., M. G. Manzato, R. M. Marcacini, C. V. Sundermann, & S. O. Rezende (2014). Using contextual information from topic hierarchies to improve context-aware recommender systems. In ICPR'14: Proceedings of the 22nd International Conference on Pattern Recognition, pp. 36063611.

Domingues, M. A., C. V. Sundermann, M. G. Manzato, R. M. Marcacini, & S. O. Rezende (2014). Exploiting text mining techniques for contextual recommendations. In WI'14:

IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, Volume 2, pp. 210217.

Everitt, B. S., S. Landau, M. Leese, & D. Stahl (2011). Cluster Analysis (5th ed.).

London, UK: Wiley Publishing.

Finkel, J. R., T. Grenager, & C. Manning (2005). Incorporating non-local information into information extraction systems by gibbs sampling. In ACL'05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 363370. Association for Computational Linguistics.

Hariri, N., B. Mobasher, R. Burke, & Y. Zheng (2011). Context-aware recommendation based on review mining. In ITWP'11: Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, pp. 3036.

Jones, S. & G. W. Paynter (2002). Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications. Journal of the American Society for Information Science and Technology 53 (8), 653677.

Laerty, J. D., A. McCallum, & F. C. N. Pereira (2001). Conditional random elds:

Probabilistic models for segmenting and labeling sequence data. In ICML'01: Proceed-ings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, USA, pp. 282289. Morgan Kaufmann Publishers Inc.

Lee, D., S. E. Park, M. Kahng, S. Lee, & S. G. Lee (2010). Exploiting contextual informa-tion from event logs for personalized recommendainforma-tion. In Computer and Informainforma-tion Science 2010, Volume 317 of Studies in Computational Intelligence, pp. 121139. Ya-magata, Japan: Springer Berlin Heidelberg.

Levi, A., O. Mokryn, C. Diot, & N. Taft (2012). Finding a needle in a haystack of reviews:

Cold start context-based hotel recommender system. In RecSys'12: Proceedings of the Sixth ACM Conference on Recommender Systems, New York, NY, USA, pp. 115122.

ACM.

Li, Y., J. Nie, & Y. Zhang (2010). Contextual recommendation based on text mining.

In COLING'10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 692700.

Linden, G., B. Smith, & J. York (2003). Amazon.com recommendations: Item-to-item collaborative ltering. IEEE Internet Computing 7 (1), 7680.

Marcacini, R. & S. O. Rezende (2013). Incremental hierarchical text clustering with privileged information. In DocEng'13: Proceedings of the 2013 ACM Symposium on Document Engineering, pp. 231232.

Marcacini, R. M. & S. O. Rezende (2010). Torch: a tool for building topic hierarchies from growing text collections. In WebMedia'10: 8th Brazilian Symposium on Multimedia and the Web - IX Workshop on Tools and Applications.

Panniello, U. & M. Gorgoglione (2012). Incorporating context into recommender sys-tems: An empirical comparison of context-based approaches. Eletronic Commerce Research 12 (1), 130.

Ricci, F., L. Rokach, B. Shapira, & P. B. Kantor (Eds.) (2011). Recommender System Handbook. Springer.