Hello Mahout Group,
which algorithm will calculate top popular numbers of items in Item
Based Recommendation.?
if my sample dataset structure is given below.
*Vignesh Prajapati*
Tel: 9427415949 | www.vipras.com.co.in
MYTK [image: Facebook] <https://www.facebook.com/vigs143> [image:
Twitter]<http://twitter.com/#%21/vigs143> [image:
LinkedIn] <http://www.linkedin.com/pub/vignesh-prajapati/37/756/46a> [image:
about.me] <http://www.way4fun.tk><http://r1.wisestamp.com/r/landing?promo=7&dest=http%3A%2F%2Fwww.wisestamp.com%2Femail-install%3Futm_source%3Dextension%26utm_medium%3Demail%26utm_campaign%3Dpromo_7>
VIGNESH PRAJAPATI 's gravatar image asked Feb 28 2012 at 23:29 in Mahout-User by VIGNESH PRAJAPATI

2 Answers

If you really want to know the most popular items, counting is what I recommend. Trying to get the most popular items as a side effect of other software with a very different purpose is kind of odd.
Perhaps you could say more about what you actually want to do.
Sent from my iPhone
Ted Dunning 's gravatar image answered Feb 28 2012 at 23:54 by Ted Dunning
Ted Dunning
And another, How can i calculate frequent Itemsets for above given
structure of my datasets.?
*Vignesh Prajapati*
Tel: 9427415949 | www.vipras.com.co.in
MYTK [image: Facebook] <https://www.facebook.com/vigs143> [image:
Twitter]<http://twitter.com/#%21/vigs143> [image:
LinkedIn] <http://www.linkedin.com/pub/vignesh-prajapati/37/756/46a> [image:
about.me] <http://www.way4fun.tk><http://r1.wisestamp.com/r/landing?promo=7&dest=http%3A%2F%2Fwww.wisestamp.com%2Femail-install%3Futm_source%3Dextension%26utm_medium%3Demail%26utm_campaign%3Dpromo_7>
VIGNESH PRAJAPATI 's gravatar image answered Feb 29 2012 at 16:48 by VIGNESH PRAJAPATI

Related Discussions

  • Trouble With Deriving Popular Items From Mahout in Mahout-user

  • Hello group, I am new to mahout..I am developing recommender with the help of itemsimilarity.but i am confused for generating popular items among them.which algorithm will calculate top popular items and frequently bought together items from transactional record for the purpose of displaying the recommended items to the users. if my sample dataset structure is given below. Dataset : ...

  • How To Remove Popular Items? in Mahout-user

  • Hi , is there a current way to remove the popular items in the recommendations? Something like STOP words. Thanks !...

  • Top Items In A Vector in Mahout-user

  • Hello all, I'm using SVD to reduce the dimensionality of a text corpus. When I get queries, I generate a new matrix with them (based on the dictionary of the index) and apply the same matrix transformation. Finally, I multiply (SVD'd) the index matrix by the (SVD'd) query matrix to get a similarity vector for each query. My question is, is there a class (or a command-line instruction) that...

  • Popularity Of Recommender Items in Mahout-user

  • Trying to come up with a relative measure of popularity for items in a recommender. Something that could be used to rank items. The user - item preference matrix would be the obvious thought. Just add the number of preferences per item. Maybe transpose the preference matrix (the temp DRM created by the recommender), then for each row vector (now that a row = item) grab the number of non zero preferences...

  • Top-N Recommendation With Matrix Factorization in Mahout-user

  • Hi, If one is using a matrix factorization based method, in order to generate a top-N recommendation to a user, all the unknown ratings of that user needs to be predicted (so that highest predicted N items can be recommended). If we are talking about a site with millions of items this means that to make a top-N recommendation to a user, that user's rating on millions of items need to be predicted...

  • Weighting Preferences For Particular Items In Mahout? in Mahout-user

  • Is there some way to weight particular preferences within Mahout? For example, suppose you were creating some kind of literature recommender that uses a 5-star preference scale. If you wanted to give double the weighting to preferences for novels versus preferences for short stories, what would be the best way to do it? Thanks, Jamey...

  • Mahout 0.4 Seems Recommend User's Existed Items To User. in Mahout-user

  • Hi,All Now I have a issue ,Mahout 0.4 seems recommend user's existed items to user. I remembered that Mahout has skips those user's existed items when recommend items to user. But I have not found the logic for skipping the existed items in Mahout 0.4. Can anyone confirm that or let me know where can find the logic for skipping existed items ? Best Regards,...

  • Issue About Large Number Of Items To Recommend in Mahout-user

  • Hi, I was wondering if anybody has dealt with the issue where your recommender system has to deal with a really large number of items which can be recommended, say 10 millions. It would be impractical for the recommender to predict a rating on every single items before ranking them. Can anybody point me to any papers or links for a solution? This issue also causes some problem for performance...

  • How To Get The Id List Of Items Which Belong To A Cluster. in Mahout-user

  • Hi, My situation is almost like '12.1 Finding similar users on Twitter' in Mahout in action book. In my document, there are lists of item id and its contents seperated by delimiter comma, for example like this CSV file(itemId, itemContents): 1223, sports 1344, football nike ... First I did convert this csv file to sequence file, and vectorized the sequence file with SparseVectorsFromSequenceFiles...

  • Universal Recommender. How To Rank Items Returned By Query On Three Types Of Indicators? in Mahout-user

  • Hi, Suppose we have created three types of indicators (coocurrence, content and intrinsic) and indexed them into Ellastic Search (ES). Then we query on these three types of indicators of a user to get recommended items. How does Universal Tecommender rank the items recommended based on these three types of indicators? I have gone thru the slides on Universal Recommender created by Pat. It...

  • Including "Unrecommendable" Items in Mahout-user

  • Is there any best practice for including user preferences for certain items as a Recommender input, but ensuring that those items are never included in actual recommendations? For example, suppose that Amazon wanted to produce book recommendations for you. They might still want to include your non-book purchases as inputs to the Recommender (as your taste in DVDs might be quite relevant, for...

  • Get Similar Items in Mahout-user

  • All: Can I get similar items of one item? I know ItemSimilarity is used to compute the similarity between items,but seems can't get similar items of Best Regards. I'm samsam....

  • Recommending Already Consumed Items in Mahout-user

  • Hi all, In some recommender applications the system might recommend already consumed items. For example, a hotel recommendation site might recommend hotel A to a user who already stayed at hotel A before. In order to recommend already consumed items we have to rank all of the items (consumed and unconsumed ones) My problem is how can we rank all the items? Let me explain. If ratings ...

  • Exploiting The Last Visited Items in Mahout-user

  • Hello, I played around a little bit with recommendations for anonymous users. Therefore I have simply build a preference array based on the recently visited items, like it is explained in "Mahout in Action". This seems to work out pretty well since the recent items perfectly relate the user's latest interests. However, now I want to include the most recent visited items into my main recommender...

  • Recommending Items With Temporal Restrictions in Mahout-user

  • Hi, My team is working on building a recommendation system to recommend items for the following use cases:1. Based on User similarity (using org.apache.mahout.cf.taste.hadoop.item.RecommenderJob as the Base)2. Based on item similarity The part where it gets tricky is that we have a temporal restriction on our items (they are valid only for x days). So in the ideal case, the recommender should/can...

  • Candidate Items For Different Cases in Mahout-user

  • boundary="" Hi A few questions: 1. I see that one of the parameters of the distributed co-occurrence item similarity is the name of the item similarity class. I wonder why it is an option? The all idea behind this algorithm is that the similarity is based on co-occurrences, what am I missing here? 2. If I want to use the distributed slope-one average diff job, but I do not...

  • Evaluating Recommendations With Expired Items in Mahout-user

  • Hi, I brought up this question in dev a few weeks ago. I have a recommendation algorithm that learns the similarity matrix relying on both current items, and expired ones that should not be recommended. However, AverageAbsoluteDifferenceRecommenderEvaluator compares the predicted and actual ratings for all items, expired or not. I believe the evaluation would be more realistic if it...

  • Few Users, Too Many Items in Mahout-user

  • Hi, I'm currently working on a small dataset ( about 16k ratings) with only 1500 users and 9800 items. Most users do not have a rich neighbourhood and item similarity does not work either as items are rated only a couple of times. Is there any suggestion on how we can adresss this issue besides Content Based Recommendations? Regards, Charly...

  • Treating User Demographics As (Pseudo) Items? in Mahout-user

  • Is there any precedent for treating users' demographic characteristics as items (particularly for item-based recommendation)? For example, if one were performing item-based recommendation within a bookselling site, it'd be natural to include the user:item purchases as boolean preferences. But could it also make sense to include certain user:demographic pairs as boolean preferences (e.g. user123...

  • Clustering User-items Based On Density in Mahout-user

  • Hi, This new feature on linked-in prompted the following question: http://inmaps.linkedinlabs.com/ http://gephi.org/ I have been studying the cluster recommender. I wonder, what would be the path of least resistance to clustering items based on user links? Thanks, Chris...