Is there any way to 'jitter' rather than completely change the or create identity?
i.e. in the photo of caucasian Male laughing, change his identity to another caucasian male with similar age, just slightly different face.
There are several methods to change hair color, skin, even ethnicity, but cannot find one 'jittering' the face.
Related
I have a database of user ratings of various television shows on a 1-10 scale. I've been trying to find a good way of determining how similar two user score-lists are two one another for shared shows.
It feels like the most obvious way to do this is just to take the absolute value of the difference. And then sum/average that for all shared shows. But I was reading this does not take into account how users will rate things on different scales. I saw some people saying cosine similarity is better for this sort of thing. Unfortunately, I've run into a lot of cases where that metric doesn't really make sense.
Example:
overall average of user1 = 8.1
overall average of user2 = 5.8
scores for shared shows only:
S1 = [8,8,10,10,10,10,6,8,10,5,6,10]
S2 = [5,6,7,8,9,9,4,5,9,1,2,8]
Obviously, these two people rated the shows they watched pretty differently. When I use the average difference it says they are not very similar (2.3 where 0 is the same). When I use something like the cosine similarity it says they are extremely similar (0.97 where 1 is the same).
Is there a metric that would be better suited for this kind of thing? My ultimate goal is to recommend users shows from other users that have similar tastes to them.
i wana to make simple plugin in sketchup, but first of all i need to understand how sketchup works. So i played with faces, shapes, groups and layers as little kid, but there is something that blows my mind.
Is here even posible, to have one face in two groups? Or probably layer, but group will be much better. For example, i have a house which i wana to split into two zones (house + garage for example) and i know, that between this two rooms is only one wall, but it belongs to house (living part) and to garage. I would like to have garage and house (living part), but im not able to click this one in sketchup. If needed i can provide image, but i think its enough explained.
If you add a face in a ComponentDefinition and place ComponentInstances in the model that face will exist in several places in the model. Groups are like component instances - but they are made unique when you edit them.
For more details you can read this article: http://www.thomthom.net/thoughts/2012/02/definitions-and-instances-in-sketchup/
Is it possible to use data stored in cookies or from a web browser to determine if a website's visitor is male or female.
I'd like to use this sort of data to style my site with a different color scheme for males.
It's impossible to know the user's gender for a fact (they could lie; I know many people who do often during registrations to maintain anonymity); but you can estimate based on browser history.
One person created a little hack where you can estimate gender by browser history:
http://www.mikeonads.com/2008/07/13/using-your-browser-url-history-estimate-gender/
QUOTE:
... modified the SocialHistory JS so that it polled the browser to find out which of the Quantcast top 10k sites were visited. I then apply the ratio of male to female users for each site and with some basic math determine a guestimate of your gender. The math is really quite simple, I just take:
1 / (1 + r_1 * r_2 * … * r_n)
where p_i is the ratio of men-to-women for the specific site. For example, if you had been to two sites that had a 2-1 ratio of men to women, the probability of you being female would be:
1 / (1 + 2 * 2) = 1/5 = 20%
If you had access to users' browser history you may be able to derive some method to estimate their gender.
Google Analytics also estimates gender, as well as Quantcast, others, and many cpanel software like awstats.
Users sometimes store their gender in local profiles on their PC, but without illegal spyware to sniff users, there is no clean legal way to gather this information which may or may not be stored in cookies in their browser based on the websites they registered for or are logged into which gathered their gender during registration.
Of course the very illegal way to be certain would be to access the webcam using illegal malware and then using facial recognition software you could set up an automated software to accurately determine the gender of every person who has a laptop or pc with a webcam. However unless you're some crazy hacker with no worries about getting arrested then I would avoid trying this if I were you.
I'm doing some exploratory work on recommendation systems and have been reading about collaborative filtering techniques involving user-based, item-based, and SVD algorithms. I am also trying out R's recommenderlab package.
One apparent assumption in the literature is that the user data has labelled items based on a rating scale, e.g. between 1 and 5 stars. I'm looking at problems where the user data does not have ratings but rather just transactions. For example, if I want to recommend restaurants to a user, the only data I have is how often he has visited other restaurants.
How can I convert these "transaction" counts into ratings that can be used by recommendation algorithms that expect a fixed-scale rating? One approach I thought of is simple binning:
0 stars = 0-1 visits
1 star = 2-3 visits
...
5 stars = 10+ visits
However, that doesn't seem like it would work well. For example, if someone visited a restaurant only once, he may still really love it.
Any help would be appreciated.
I would try different approaches. As you said, only visited once may indicate that the user still loves the restaurant but you don't know for sure. Your goal is not to optimize for one single user rather for all users. So for this, you can split your data into training and test data. Train on the training data with different scales and test on the test data.
The different scales may be
a binary scale (0:never visited, 1: visited). This is mostly used in online shops (bought or not). Would support your assuption with the one time visit.
your presented scale or other ranges for the 5 stars. You can also use more than 5 stars. I would potentially not group 0-1 visits.
The approach with the best accuracy should be chosen.
Here's an idea: restaurants the user has visited zero or one times tell you nothing about what they like. Restaurants they have visited many times tell you lots. Why not just look for restaurants similar to those the customer most regularly frequents? In this way, you're using positive information (what they like) but none of the negative since you don't have access to it anyway.
If you absolutely had to infer some continuous measure, I think it would only be sensible to look at the propensity for another visit given past behaviour. This would start with the prior probability of choosing that restaurant (background frequency, or just uniform over restaurants) with a likelihood term related to the number of visits to that restaurant. In this way the more a user visits a restaurant the more likely they are to visit again.
I am about to embark on a new project - a video website. Users will be able to register, and vote on videos by clicking "like" or "dislike", or something to that effect. In any event, it will be a 2-option voting system, not a 5-star system.
Every X number of days, I will be generating a "chart" of the most popular videos. So my question is: how should I determine the popularity of a given video?
If I went the route of tallying up the videos with the most views, this could have the effect of exceptionally bad videos making it to the of the charts (just because they're so bad).
If I go the route of a scoring system based on the amount of "like" and "dislike" votes (eg. 100 like votes, and 50 dislike votes equals a score of 2), videos with few views could appear on the top of the charts.
So, what I need to do is a combination of the two. Barring, of course, spammy views and votes.
What's your guys' thoughts on the subject?
Edit: the following tags were removed: [mysql] [postgresql], to make room for other, more representative tags; the SQL technology used in the intended implementation does not seem to bear much on the considerations regarding the rating model per-se.
You seem to be missing the point that likes and dislikes in movies are anything but objective even within the context of a relatively homogeneous group of "voters". Think how the term "Chix Flix" or the success story called "NetFlix", illustrate this subjectivity...
Yet, if you persist in implementing the model you suggest, there are several hidden variables and system dynamics that need to be acknowledged and possibly taken into account in the rating's formula.
the existence of a third, implicit, value of the vote: "No vote"
i.e. when someone views the movie page and yet doesn't vote, either way.
The problem of dealing with this extra value is its ambiguity: do people not vote because they didn't see the movie or because they neither truly like nor disliked it? Very likely a bit of both, therefore we can/should use the count of the "Page views without vote" in the formula, to boost (somewhat) the rating of movies that do not generate a strong (positive or negative) sentiment (lest the "polarizing" movies will appear more notorious or popular)
the bandwagon effect
Past a certain threshold, and particularly if the rating and/or vote counts is visible before the page view, the rating and vote counts can influence the way people decide to vote (either way) or even decide to abstain from voting. The implication is that the total vote and/or view counts do not relate linearly to the effective rating.
"quality" vs. "notoriety"
Vote ratios in general (eg "likes" / "total" or "likes"/"dislikes" etc.) are indicative of the "quality" of a movie (note the quotes around quality...), whereby the number of votes (and of views) is indicative of the notoriety ("name recognition" etc.) of a movie.
statistical representativity
Very small vote and/or view counts are to be handled carefully because they introduce much volatility in the rating. Phrased otherwise, small samples make for not so statically representative ratings.
trends (the time variable)
At the risk of complicating the model, consider keeping [some] record of when votes/view happened, to allow identifying "hot" (and "cooling") movies in the collection. This info may inform the rating logic, but also may be used to direct the users towards currently hot items. BTW, hence feeding the bandwagon effect mentioned :-( but also, increasing the voting sample size :-).
All these considerations suggest caution in implementing this rating system. It also hints at the likely need of including statistics about the complete set of movies into the rating formula for an individual movie. In other words, do not rate a given movie solely on the basis of the its own vote/view counts but also on say the average vote counts a move receives, the maximum view a movie page gets etc. In fact, an iterative process, whereby movies are [roughly] ranked at first and then the ranking is recalculated by using the statistics of groups of movies similarly rated may provide a better system (provided the formulas are "fair" and somehow converge)
A standard trick is to start with a neutral baseline: say 10 likes and 10 dislikes that gives a score of 1. The first few votes don't change the ratio too much, but as votes accumulate, the baseline is overwhelmed. The exact choice of the baseline values will influence the rating of a new movie (the two values don't have to be equal), and how many votes are needed to change the rating substantially.