Thanks, Sean. Below is my code.
AbstractJDBCDataModel preference_model = new MySQLJDBCDataModel(ds,
table_name, colum1, colum2,colum3);
DataModel model = new GenericDataModel(preference_model.exportWithPrefs());
ItemSimilarity itemSimilarity = null;
try {
itemSimilarity = new PearsonCorrelationSimilarity(model);
} catch (TasteException e) {
e.printStackTrace();
}
Recommender itembased_recommender = null;
try {
itembased_recommender = new CachingRecommender(new
GenericItemBasedRecommender(model, itemSimilarity));
} catch (TasteException e) {
e.printStackTrace();
}
Scanner sc = new Scanner(System.in);
while (true) {
String nextLine = sc.nextLine();
long user_id = 0;
try {
user_id = Long.parseLong(nextLine);
} catch (Exception e) {
continue;
}
long start = System.currentTimeMillis();
List<RecommendedItem> itembased_items = null;
try {
itembased_items = itembased_recommender.recommend(user_id, 10);
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
long end = System.currentTimeMillis();
System.out.println("Time Used:" + (end - start) + "ms");
}
}
>That doesn't sound nearly right -- there must be a problem in your
>implementation. THere is no way a recommendation takes 6 minutes. I am
>guessing you are actually triggering a reload, but even that doesn't
>make sense that it would take so long. You'd have to provide a lot
>more info about your code, algorithm and data to get more comments
>though.
>
>2010/7/14 Young <[email protected]>:
>> Hi,
>> I have the same problem. I did not re-compute the similarity of items. But
>> when implementing itembased_recommender.recommend(user_id, 10), time varies
>> a lot.
>> If the user gave 3 ratings, it takes 620ms.
>> If the user gave 30 ratings, it takes 369367ms.
>>
>> How can I do to make it real time recommendation?
>> Thanks very much
>>
>>
>>
>>>Yes, you do not need to refresh this item-item similarity matrix. The
>>>default implementations don't do something like this so I believe you
>>>just need to adjust your process to not recompute this on each new
>>>user.
>>>
>>>It should not be required anyway; a new user entering the system does
>>>not need a refresh of item-item similarity. A new *item* might, but,
>>>it is no big deal to be missing this item for some short time until
>>>the next refresh.
>>>
>>>If you are reloading a large data set periodically you probably want
>>>some system to be constructing the new instance while the old one
>>>serves, then switch instances when the new one is ready. Then it
>>>doesn't matter much how long it takes to load.
>>>
>>>Please show a stack trace for NoSuchUserException, at least the
>>>portion in the library.
>>>
>>>On Tue, Jul 13, 2010 at 6:41 AM, samsam <[email protected]> wrote:
>>>> Hi again. I'm getting a really hard time to use Taste using the Large 10M
>>>> dataset. Using FileDataModel is taking too long to recompute the
>>>> sim-matrix.
>>>> I'm assuming a new user has just arrived (not on the user data or item
>>>> data)
>>>> and starts voting. Trying to refresh the matrix takes over 25 seconds a
>>>> large time when considering a website for instance.
>>>>
>>>> So I found this class. I tried to use it just like shown on javadocs:
>>>>
>>>> PlusAnonymousUserDataModel plusmodel = new
>>>> PlusAnonymousUserDataModel(datamodel);
>>>> PearsonCorrelationSimilarity sim = new
>>>> PearsonCorrelationSimilarity(datamodel);
>>>> Recommender recommender = new GenericItemBasedRecommender(datamodel,sim);
>>>> PreferenceArray pref = new GenericUserPreferenceArray(10);
>>>> pref.setUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
>>>> for(int i=0;i<10;i++){
>>>> pref.setItemID(i, votes[i][0]);
>>>> pref.setValue(i,votes[i][1]);
>>>> }
>>>> synchronized(pref) {
>>>> plusmodel.setTempPrefs(pref);
>>>> recommender.recommend(PlusAnonymousUserDataModel.TEMP_USER_ID,
>>>> 10);
>>>> }
>>>>
>>>> But this is causing an NoSuchUserException at the recommender.
>>>>
>>>> I tried using the plusmodel on similarity and recommender instead of the
>>>> realmodel, but did not work as well.
>>>>
>>>>
>>>>
>>>> On Mon, Jul 5, 2010 at 11:52 PM, Sean Owen <[email protected]> wrote:
>>>>
>>>>> Pre-compute the similarity based on what information? You mention that
>>>>> you don't want to use Pearson and mention item attributes.
>>>>>
>>>>> If you are trying to use domain-specific attributes of items, then
>>>>> it's up to you to write that logic. If you want to say books have a
>>>>> "0.5" similarity when they are within the same genre, and "0.9" when
>>>>> by the same author, you can just write that logic. That's not part of
>>>>> the framework.
>>>>>
>>>>> The hook into the framework comes when you implement ItemSimilarity
>>>>> with logic like that. Then just use that ItemSimilarity instead of one
>>>>> of the given implementations. That's all.
>>>>>
>>>>> On Mon, Jul 5, 2010 at 4:32 PM, samsam <[email protected]> wrote:
>>>>> > About the second question,I have not the similarity,I want to know is
>>>>> > how
>>>>> to
>>>>> > pre-compute the item similarity.
>>>>> >
>>>>> > On Mon, Jul 5, 2010 at 11:20 PM, Sean Owen <[email protected]> wrote:
>>>>> >
>>>>> >> 1) Good question. One answer is to make these "anonymous" users real
>>>>> >> users in your data model, at least temporarily. That is, they need not
>>>>> >> be anonymous to the recommender, even if they're not yet a registered
>>>>> >> user as far as your site is concerned.
>>>>> >>
>>>>> >> There's a class called PlusAnonymousUserDataModel that helps you do
>>>>> >> this. It wraps a DataModel and lets you quickly add a temporary user,
>>>>> >> recommend, then un-add that user. It may be the easiest thing to try.
>>>>> >>
>>>>> >> (BTW the book Mahout in Action covers this in section 5.4, in the
>>>>> >> current MEAP draft.)
>>>>> >>
>>>>> >> 2) Not sure I fully understand. You already have some external,
>>>>> >> pre-computed notion of item similarity? then just feed that in to
>>>>> >> GenericItemSimilarity and use it from there.
>>>>> >>
>>>>> >> Sean
>>>>> >>
>>>>> >> On Mon, Jul 5, 2010 at 1:52 PM, samsam <[email protected]> wrote:
>>>>> >> > Hello,all
>>>>> >> > I want to build recommendation engine with apache mahout,I have read
>>>>> some
>>>>> >> > reading material,and I still have some questions.
>>>>> >> >
>>>>> >> > 1)How to recommend for anonymous users
>>>>> >> > I think recommendation engine should return recommendations given a
>>>>> item
>>>>> >> > id.For example,a anonymous user reviews some items,
>>>>> >> > and tell the recommendation what he reviews,and compute with the
>>>>> reviews
>>>>> >> > histories.
>>>>> >> >
>>>>> >> > 2)How to compute the items similarity dataset
>>>>> >> > Without use items similarity dataset,we can make ItemBasedRecommender
>>>>> >> > with PearsonCorrelationSimilarity,but
>>>>> >> > we need to make recommendations with extra attributes of items,
>>>>> >> > so we should use the items similarity dataset,how to build the
>>>>> >> > dataset
>>>>> is
>>>>> >> > the key point.
>>>>> >> > --
>>>>> >> > I'm samsam.
>>>>> >> >
>>>>> >>
>>>>> >
>>>>> >
>>>>> >
>>>>> > --
>>>>> > I'm samsam.
>>>>> >
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> I'm samsam.
>>>>
>>
