Thanks Sean.
- When I used GenericUserBasedRecommender in code 2 I got 0 , but when
using GenericBooleanPrefUserBasedRecommender both MAE and RMSE in case
2 gave me scores, so only RMSE is not useful or also MAE ?
- If I want to compare between recommenders that use preferences and
those that don't use , does using code 3 and 4 below with
GenericRecommenderIRStatsEvaluator makes sense? Since using code 2
with GenericBooleanPrefUserBasedRecommender creates different
recommender that uses weights.
//--- Code 3 -----
DataModel model = new FileDataModel(new File("ua.base"));
RecommenderIRStatsEvaluator evaluator = new
GenericRecommenderIRStatsEvaluator();
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
public Recommender buildRecommender(DataModel model) throws
TasteException {
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood = new
NearestNUserNeighborhood(k, similarity, model);
return new GenericUserBasedRecommender(model, neighborhood,
similarity);
}};
//--- Code 4 ---
DataModel model = new GenericBooleanPrefDataModel(
GenericBooleanPrefDataModel.toDataMap(
new FileDataModel(new File("ua.base"))));
RecommenderIRStatsEvaluator evaluator = new
GenericRecommenderIRStatsEvaluator();
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
public Recommender buildRecommender(DataModel model) throws
TasteException {
UserSimilarity similarity = new LogLikelihoodSimilarity(model);
UserNeighborhood neighborhood = new
NearestNUserNeighborhood(k, similarity, model);
return new GenericUserBasedRecommender(model, neighborhood,
similarity);
}};
On Tue, Jan 22, 2013 at 1:58 AM, Sean Owen <[email protected]> wrote:
> No it's really #2, since the first still has data that is not
> true/false. I am not sure what eval you are running, but an RMSE test
> wouldn't be useful in case #2. It would always be 0 since there is
> only one value in the universe: 1. No value can ever be different from
> the right value.
>
> On Tue, Jan 22, 2013 at 4:34 AM, Zia mel <[email protected]> wrote:
>> Hi !
>>
>> Can we say that both code 1 and 2 below are using boolean recommender
>> since they both use LogLikelihoodSimilarity? Which code is used by
>> default when no preferences are available ? When using
>> GenericUserBasedRecommender in code 1 it gave a score during
>> evaluation , but when using it in code 2 it gave 0 , is the score
>> given by code 1 correct since in MAI book page 23 said "In the case of
>> Boolean preference data, only a precision-recall test is available
>> anyway".
>>
>> //-- Code 1 --
>> DataModel model = new GroupLensDataModel(new File("ratings.dat"));
>> RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
>> public Recommender buildRecommender(DataModel model) throws
>> TasteException {
>> UserSimilarity similarity = new LogLikelihoodSimilarity(model);
>> UserNeighborhood neighborhood = new
>> NearestNUserNeighborhood(2, similarity, model);
>> return new GenericUserBasedRecommender(model, neighborhood,
>> similarity);
>> }};
>>
>> //--- Code 2 ---
>> DataModel model = new GenericBooleanPrefDataModel(
>> GenericBooleanPrefDataModel.toDataMap(
>> new FileDataModel(new File("ua.base"))));
>>
>> RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
>> public Recommender buildRecommender(DataModel model) throws
>> TasteException {
>> UserSimilarity similarity = new LogLikelihoodSimilarity(model);
>> UserNeighborhood neighborhood = new
>> NearestNUserNeighborhood(2, similarity, model);
>> return new GenericBooleanPrefUserBasedRecommender (model,
>> neighborhood, similarity);
>> }};
>>
>> Many Thanks !
