So I got a problem that I can't wrap my mind around.
I'm creating a shopping list that is divided into ten categories of various lengths. (All of the items come from a database). I got it to work when using a single column, but I have to divide the list into four columns. The code should decide which categories should go where so that the four columns have the most equal number of items possible.
This is what the list will look like when the code is working.
Out of these ten categories, four of them have a specific category they belong to.
The way I've approached this is to count the total number of items and divide it by four to compute the average number of items per column. I put the four special categories in their respective column and kept track of how many items were now in each column.
Now I still have six columns remaining of various sizes. What is the best approach to put them in the column that would fit best? Since some categories are much larger than others, some columns could potentially have three or four categories.
UPDATE: Right after I posted this I came to the realization that I should find the column with the least items and add the largest category to it. This seems like it will work. And it looks like Dave is suggesting the same!
After writing your 4 "main" categories to the columns, make an array that has a total of each column:
$columnTotals = array(10,6,12,13)
//example - obviously you'd use count or something to get the totals
Then, order your non-special categories in an array by largest to smallest:
$subcatTotals = array(18,15,13,12,8,4);
//here, you'll have to get the totals, then use an array sort to order them
//probably want an associative array so you know which total matches which cat.
Then, in a loop, add the first(largest) sub-category to the smallest column, and get a new total for that column.
This SHOULD give you the most even columns you can get - at least it has in all the made-up examples I've tried it with.
Your approach is most ideal in today's context. Let me explain...
The ideal thing to do right now is do your little calculation and split the list into the number of rows & columns.
The alternative is a CSS3 approach. i.e., you can create the whole list in ONE column through PHP. And on the CSS side, you can specify the new property "column-count".
But there are issues. This is not yet properly standardised. So you've got to specify the -moze- prefix and -webkit- prefix depending on your browser. But the reason I wouldn't go for this is that IE still does not support this. And it's too early to consider an upgrade by all users even if they did.
Going one step further, you ought to modify your splitting algorithm to take into account the category headings.
Hope this helps :)
Related
I am looking for an optimized way of displaying dynamically generated items sorted and grouped based on initials (like the image below). Groups can have different number of items and therefore also the total number of items is not known.
What would be the best solution to spread the groups across columns(they should remain sorted)?
Currently the groups and items are generated from php and spread across columns(divs) which contain the groups and items within ul and li items.
https://www.dropbox.com/s/ub3mg3twm0eg8b6/columns.jpg
Thanks in advance,
You can use CSS3 columns https://developer.mozilla.org/en-US/docs/Web/Guide/CSS/Using_multi-column_layouts and this will work as a polyfill https://github.com/BetleyWhitehorne/CSS3MultiColumn
Since it looks you just need a push to get started he's what I would do. Since your columns are dynamically generated I assume you're using PHP for that you should:
Count each item in the columns
Count all items on the whole page
Divide that by three
Next you'd output the first third until the last item of the last group
Count all the items in the first column
Next output starting at the first item of the new group and output as many items as in the first group excluding the last group
Output the rest of the items in de last column
Needs some tweaking of course. Good luck!
I'm currenlty building a webshop. This shop allows users to filter products by category, and a couple optional, additional filters such as brand, color, etc.
At the moment, various properties are stored in different places, but I'd like to switch to a tag-based system. Ideally, my database should store tags with the following data:
product_id
tag_url_alias (unique)
tag_type (unique) (category, product_brand, product_color, etc.)
tag_value (not unique)
First objective
I would like to search for product_id's that are associated with anywhere between 1-5 particular tags. The tags are extracted from a SEO-friendly url. So I will be retrieving a unique strings (the tag_url_alias) for each tag, but I won't know the tag_type.
The search will be an intersection, so my search should return the product_id's that match all of the provided tags.
Second objective
Besides displaying the products that match the current filter, I would also like to display the product-count for other categories and filters which the user might supply.
For instance, my current search is for products that match the tags:
Shoe + Black + Adidas
Now, a visitor of the shop might be looking at the resulting products and wonder which black shoes other brands have to offer. So they might go to the "brand" filter, and choose any of the other listed brands. Lets say they have 2 different options (in practice, this will probably have many more), resulting in the following searches:
Shoe + Black + Nike > 103 results
Shoe + Black + K-swiss > 0 results
In this case, if they see the brand "K-swiss" listed as an available choise in their filter, their search will return 0 results.
This is obviously rather disappointing to the user... I'd much rather know that switching the "brand" from "adidas" to "k-swiss" will 0 results, and simply remove the entire option from the filter.
Same thing goes for categories, colors, etc.
In practice this would mean a single page view would not only return the filtered product list described in my primary objective, but potentially hundreds of similar yet different lists. One for each filter value that could replace another filter value, or be added to the existing filter values.
Capacity
I suspect my database will eventually contain:
between 250 and 1.000 unique tags
And it will contain:
between 10.000 and 100.000 unique products
Current Ideas
I did some Google searches and found the following article: http://www.pui.ch/phred/archives/2005/06/tagsystems-performance-tests.html
Judging by that article, running hundreds of queries to achieve the 2nd objective, is going to be a painfully slow route. The "toxy" example might work for my needs and it might be acceptable for my First objective, but it would be unacceptably slow for the Second objective.
I was thinking I might run individual queries that match 1 tag to it's associated product_id's, cache those queries, and then calculate intersections on the results. But, do I calculate these intersections in MySQL? or in PHP? If I use MySQL, is there a particular way I should cache these individual queries, or is supplying the right indexes all I need?
I would imagine it's also quite possible to maybe even cache the intersections between two of these tag/product_id sets. The amount of intersections would be limited by the fact that a tag_type can have only one particular value, but I'm not sure how to efficiently manage this type of caching. Again, I don't know if I should do this in MySQL or in PHP. And if I do this in MySQL, what would be the best way to store and combine this type of cached results?
Using sphinx search engine can make this magic for you. Its is VERY fast, and even can handle wordforms, what can be useful with SEO requests.
In terms of sphinx, make a document - "product", index by tags, choose proper ranker for query (ex, MATCH_ALL_WORDS) and run batch request with different tag combinations to get best results.
Dont forget to use cachers like memcahed or any other.
I did not test this yet, but it should be possible to have one query to satisfy your second objective rather than triggering several hundred queries...
The query below illustrates how this should work in general.
The idea is to combine the three different requests at once and group by the dedicated value and collect only those which have any results.
SELECT t1.product_id, count(*) FROM tagtable t1, tagtable t2, tagtable t3 WHERE
t1.product_id = t2.product_id AND
t2.product_id = t3.product_id AND
t1.tag_type='yourcategoryforShoe' AND t1.tag_value='Shoe' AND
t2.tag_type='product_color' AND t2.tag_value='Black' AND
t3.tag_type='brand'
GROUP BY t3.tag_value
HAVING count(*) > 0
I'm searching algorithm, or some fitness rating method.
As an example take Stackoverflow. Posts are divided to groups by
Rating (+,-,0)
Tags (and tags importance based on activity in them)
Users (user rating/reputation, age, recent activity)
Keywords
And I'm looking for way, how to sort them to create optimized/balanced mix.
I don't want to show ONLY the newest OR ONLY the top rated OR ONLY important tags
maybe the name would be "Multiple-attributes optimal sorting", or something similar.
Anyone can advise something?
Thanks
ADD1: maybe we are talking about Fitness function ( http://en.wikipedia.org/wiki/Fitness_function )
Generate separate sub-scores for each of those factors, then normalize them, add them together, and sort by the resulting total for each post. For instance,
Rank all of the posts by rating, and then map their position in the ranking to a 0.0-1.0 range (highest rated post is 1.0, lowest is 0.0).
Create a function to take a post's tags and calculate a similar 0.0-1.0 score based on tags only.
Create another function to do the same for the user.
And another for any keywords you want.
If you want certain things to factor in more than others, multiply the subscore by a constant factor before adding it to the total - for instance, if you want rating to be important, and the others less so, you might do (3*A)+B+C+D if the four subscores are the letters.
As for exactly how you translate things into subscores? That's something you really have to determine for your particular app; there's no single way of doing it that is "right".
PHP / MySQL backend. I've got a database full of movies YouTube-style. Each video has a name and category. Videos and categories have a m:n relationship.
I'd like for my visitors to be able to search for videos and have them enter the search terms in one search field. I can't figure out how to return the best search results based on being category, occurrences in name.
What's the best way to go about something like this? Scoring? => Check for each search term whether it occurs in the name of the video; if so, award the video a point; check if the video is in categories that are also contained in the search query; if so, award it a point. Sort it by number points received? That sounds very expensive in terms of CPU usage.
Using Full-Text Search may help: http://dev.mysql.com/doc/refman/5.0/en/fulltext-search.html#function_match
You can test several columns at once against an expression.
First, use full text search. It can be either MySql full-text search or some kind of extrenal full-text search engine. I recommend sphinx. It is very fast, simple and even can be integrated with MuSQL using SphinxSE (so search indexes look loke tables in MySQL). However you have to install and configure it.
Second, think about splitting search results by search type. Any kind of full-text search will return list of matched items sorted by relevancy. You can search by all fields and get a single list. This is bad idea because hits by name and hits by category will be mixed. To solve this you can do multiple searches - search by name first, then search by category.
As a result you'll have two matching sets and you have a lot of options how to display this. Some ideas:
merge 2 sets based on relevancy rate returned by the search engine. This looks like result of one single query but you know what each item is (name hit or category hit) so you can highlight this
do the same marge as above but assign different weights to different sets, for eaxmple relevancy = 0.7*name_relevancy+0.3*category_relevancy. This will make search results more natural
spit results into tabs/groups e.g. 'There are N titles and M categories matching your query)
Use bands when displaying results. For each page (assuming you are splitting search results using paginator) dispslay N items from the first set and M items from the second set (you can dipslya sets one by one or shuffle items). If there is no enough items in one of sets then just get more items from another set, so there is always M+N items per page
Any other way you can imagine
And you can use this method for any kind of fields - name, categroy, actor, director, etc. However the more fields you use the more search queries you have to execute
I don't think you can avoid looking at the title and category of every movie for each search. So the CPU usage for that is a given. If you are concerned about the CPU usage of the sort, it would be negligible in most cases, since you would only be sorting the items that have more than zero points.
Having said that, what you probably want is a system that is partially rule-based and partially point-based. For instance, if you have a title that is equal to the search term, it should come first, regardless of points. Architect your search such that you can easily add rules and tweak points as you see fit to yield the best results.
Edit: In the event of an exact title match, you can take advantage of a DB index and not search the whole table. Optionally, the same goes for category.
we often see 'related items'. For instance in blogs we have related posts, in books we have related books, etc. My question is how do we compile those relevency? If it's just tag, I often see related items that does not have the same tag. For instance, when search for 'pink', a related item could have a 'purple' tag.
Anyone has any idea?
There are many ways to calculate similarity of two items, but for a straightforward method, take a look at the Jaccard Coefficient.
http://en.wikipedia.org/wiki/Jaccard_index
Which is: J(a,b) = intersection(a,b)/union(a,b)
So lets say you want to compute the coefficient of two items:
Item A, which has the tags "books, school, pencil, textbook, reading"
Item B, which has the tags "books, reading, autobiography"
intersection(A,B) = books, reading
union(A,B) = books, school, pencil, textbook, reading, autobiography
so J(a,b) = 2/6 = .333
So the most related item to A would be the item which results in the highest Jaccard Coefficient when paired with A.
Here are some of the ways:
Manually connecting them. Put up a table with the fields item_id and related_item_id, then make an interface to insert the connections. Useful to relate two items that are related but have no resemblance or do not belong to the same category/tag (or in an uncategorized entry table). Example: Bath tub and rubber ducky
Pull up some items that belong to the same category or have a similar tag. The idea is that those items must be somewhat related since they are in the same category. Example: in the page viewing LCD monitors, there are random LCD monitors (with same price range/manufacturer/resolution) in the "Related items" section.
Do a text search matching current item's name (and or description) against other items in the table. You get the idea.
To get a simple list of related items based on tags, the basic solutions goes like this:
3 tables, one with items, one with tags and one with the connection. The connection table consists of two columns, one for each id from the remaining tables. An entry in the connection table links a tag with an item by putting their respective ids in a row.
Now, to get that list of related items.
fetch all items which share at least one tag with the original item. be sure to fetch the tags along with the items, and then use a simple rating mechanism to determine, which item shares the most tags with the original one. each tag increases the relation-relevancy by one.
Depending on your tagging-habits, it might be smart to add some counter-mechanism to prevent large overarching tags from mixing up the relevancy. to achieve this, you could give greater weight to tags below a certain threshold of appliances. A threshold which has generally worked nicely for me, is total_number_of_tag_appliances/total_number_of_tags, which results in the average number of appliances. If the tags appliance-count is smaller than average, the relation-relevancy is increased double.
It can be more than a tag, for example it can be average of each work appearing in a paragraph, and then titles, etc
I would say they use ontology for that which adds more great features to the application.
it can also be based on "people who bought this book also bought"
No matter how, you will need some dort of connection between your items, and they will mostly be made by human beings
This is my implementation(GIST) of Jaccard index with PostgreSQL, and Ruby on Rails...
Here is an implementation of jaccard index between two texts based on bigrams.
https://packagist.org/packages/darkopetreski/textcategorization