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Posted to dev@mahout.apache.org by Ted Dunning <te...@gmail.com> on 2011/09/11 21:55:55 UTC
Re: Recommendation with a dataset with no/same preference
Binary preferences are fine.
In fact, I generally recommend that all ratings and related information be
distilled down to a single binary indicator such as you already have.
The fact that you have so few items will be both your advantage and
disadvantage. It will help you avoid problems with sparsity and lack of
overlap between users, but it will also make your life harder because theere
aren't so many items to recommend. This will be exacerbated by your
customers' tendency to exhaustively research items before purchase ... it is
likely that they will know about most related items already.
On Sun, Sep 11, 2011 at 10:01 AM, Manju <ma...@yahoo.com> wrote:
> ... have purchase data but not rating data ...
>
> Any advice on how best to approach the scenario with item or user based
> recommendation (given the lack of spread in ratings/preferences)?
>
>
Re: Recommendation with a dataset with no/same preference
Posted by Ted Dunning <te...@gmail.com>.
Good luck.
Let us know how it turns out.
On Sun, Sep 11, 2011 at 2:55 PM, Manju <ma...@yahoo.com> wrote:
> Ted and Sean,
> Thanks for the suggestion/advice. My prototype ran successfully
> (programatically:) with GenericBooleanPrefItemBasedRecommender. I am
> reviewing/reflecting on the output.
> Thanks again.
> Manju
>
> ------------------------------
> *From:* Ted Dunning <te...@gmail.com>
> *To:* user@mahout.apache.org; Manju <ma...@yahoo.com>
> *Cc:* "dev@mahout.apache.org" <de...@mahout.apache.org>
> *Sent:* Sunday, September 11, 2011 3:55 PM
> *Subject:* Re: Recommendation with a dataset with no/same preference
>
> Binary preferences are fine.
>
> In fact, I generally recommend that all ratings and related information be
> distilled down to a single binary indicator such as you already have.
>
> The fact that you have so few items will be both your advantage and
> disadvantage. It will help you avoid problems with sparsity and lack of
> overlap between users, but it will also make your life harder because theere
> aren't so many items to recommend. This will be exacerbated by your
> customers' tendency to exhaustively research items before purchase ... it is
> likely that they will know about most related items already.
>
> On Sun, Sep 11, 2011 at 10:01 AM, Manju <ma...@yahoo.com> wrote:
>
> ... have purchase data but not rating data ...
>
>
>
> Any advice on how best to approach the scenario with item or user based
> recommendation (given the lack of spread in ratings/preferences)?
>
>
>
>
Re: Recommendation with a dataset with no/same preference
Posted by Ted Dunning <te...@gmail.com>.
Good luck.
Let us know how it turns out.
On Sun, Sep 11, 2011 at 2:55 PM, Manju <ma...@yahoo.com> wrote:
> Ted and Sean,
> Thanks for the suggestion/advice. My prototype ran successfully
> (programatically:) with GenericBooleanPrefItemBasedRecommender. I am
> reviewing/reflecting on the output.
> Thanks again.
> Manju
>
> ------------------------------
> *From:* Ted Dunning <te...@gmail.com>
> *To:* user@mahout.apache.org; Manju <ma...@yahoo.com>
> *Cc:* "dev@mahout.apache.org" <de...@mahout.apache.org>
> *Sent:* Sunday, September 11, 2011 3:55 PM
> *Subject:* Re: Recommendation with a dataset with no/same preference
>
> Binary preferences are fine.
>
> In fact, I generally recommend that all ratings and related information be
> distilled down to a single binary indicator such as you already have.
>
> The fact that you have so few items will be both your advantage and
> disadvantage. It will help you avoid problems with sparsity and lack of
> overlap between users, but it will also make your life harder because theere
> aren't so many items to recommend. This will be exacerbated by your
> customers' tendency to exhaustively research items before purchase ... it is
> likely that they will know about most related items already.
>
> On Sun, Sep 11, 2011 at 10:01 AM, Manju <ma...@yahoo.com> wrote:
>
> ... have purchase data but not rating data ...
>
>
>
> Any advice on how best to approach the scenario with item or user based
> recommendation (given the lack of spread in ratings/preferences)?
>
>
>
>
Re: Recommendation with a dataset with no/same preference
Posted by Manju <ma...@yahoo.com>.
Ted and Sean,
Thanks for the suggestion/advice. My prototype ran successfully (programatically:) with GenericBooleanPrefItemBasedRecommender. I am reviewing/reflecting on the output.
Thanks again.
Manju
________________________________
From: Ted Dunning <te...@gmail.com>
To: user@mahout.apache.org; Manju <ma...@yahoo.com>
Cc: "dev@mahout.apache.org" <de...@mahout.apache.org>
Sent: Sunday, September 11, 2011 3:55 PM
Subject: Re: Recommendation with a dataset with no/same preference
Binary preferences are fine.
In fact, I generally recommend that all ratings and related information be distilled down to a single binary indicator such as you already have.
The fact that you have so few items will be both your advantage and disadvantage. It will help you avoid problems with sparsity and lack of overlap between users, but it will also make your life harder because theere aren't so many items to recommend. This will be exacerbated by your customers' tendency to exhaustively research items before purchase ... it is likely that they will know about most related items already.
On Sun, Sep 11, 2011 at 10:01 AM, Manju <ma...@yahoo.com> wrote:
... have purchase data but not rating data ...
>
Any advice on how best to approach the scenario with item or user based recommendation (given the lack of spread in ratings/preferences)?
>
>