I'm evaluating Magento for a travel company who will need to do product searches and recommendations based on geographical distance. The company is creating custom holiday packages based on various components (eg: accommodation, tours, restaurant vouchers, etc). These components potentially have overlapping locations (ie: a particular tour might be close enough to several hotels to be considered related to each of them).
As a user builds up their custom package by adding stays at various hotels, I'd like related product recommendations to appear based on geographical location. And, if they search for tours, I'd like closer tours to be weighted toward the top of the catalogue search results.
Nice to have: the ability for the user to select how close / far they consider "close enough" to be (eg: 10km, 50km, 200km, etc).
My research indicates there isn't out of the box support for any sort of spatial queries in Magento. The best solution I could come up with was custom product attributes which list "location" where each product is manually assigned to various locations. But I think that's going to get pretty hard to manage for more than ~50 locations. Is my research correct? Is there an add-on / extension which will fulfil this scenario? Do you think overlapping 50 locations will be manageable in the backend?
Coming from a Microsoft background, my natural inclination would be to enable SQL Server 2008+ spacial functionality and do the queries in the database. Obviously, this option isn't available in the LAMP stack. Am I wrong? Does MySQL support spatial queries like WHERE productA.Location.GetDistanceFrom(productB.Location) < 50km?
Mysql supports spatial queries as well http://dev.mysql.com/doc/refman/4.1/en/spatial-extensions.html but nothing will help you or free you from entering the relations between products and it's location and you have to implement it yourself as well as extend the search based on location
Related
I'm starting an online business targeted at a particular demographic and interests so I would like to produce content targeted at what this particular target market are actually searching for.
Google Ads allowed me to refine my target audience to the exact categories (demographics and interests) I needed but I couldn't tell me what that category of people tend to search for except for the tiny subset that happens to click on one of my ads which is very rare given I am just starting with a small budget. I would like to know the most popular search terms for everyone in the categories I specified not just those who happened to click on my ads.
I tried Google Trends, that told me the popularity of a particular search term for a given country but that's too broad - I need to narrow it down to a particular city, age group, parental status and interests. Google Trends also helped me find popular related search terms given a particular search term so I could try using that to see if there are any common popular related search terms related to my guesses but I could miss terms related to terms I never thought of.
I could try producing content across a rage of topics which I think my target audience might be interested in and then analyse the results using Google Ads but that could be a very expensive trial and error process and I might miss more popular topics which I never thought of.
Of course I could try to ask my target market in person directly (by interrupting people in the street!) but that would be very expensive for me because I would have to travel to and stay at the location where my online business is targeted, hoping to meet people with the exact same demographic and interests that I am looking.
I'm sure there must be a way to figure this out using the the Google search analytics. Essentially, all I need is a list of most popular recent Google search terms for a particular location, demographic and interests group in Google Analytics. Could anyone help me understand how to get this list?
Here are a few considerations, even if you found an answer.
Take a look at the AdRoll platform. Here's a potentially helpful article from them about target audience and demographics.
A recent article about AdWords demographic targeting. An older looking article, connecting demographics to search queries, but page's source code suggests an update this year.
Last but not least, you're probably eligible to talk with a Google Small Business Advisor.
I am building a product catalog for an e-comm website. I am having a requirement to build a azure search/solr/elastic search based index. The problem is saving the market specific attributes. The website is supporting 109 markets and there is each market specific data like ratings, price, views, wish-listed, etc. that I need to save in the index eg: Product1 will have 109 ratings(rating is different in each market)/109 prices(price might be different in each market) corresponding to 109 markets. Also I will have to use this attributes to add a boosting function so that when people are searching for this, products with higher view/ratings surfaces up. How do I design the index definition to support this? Can I achieve this by 1 index doc per product or do I have to create 1 index doc per market? Some pointers will be very helpful. I have spent couple of days on this and could not reach to a conclusion that is optimized for this use case. Thank you!
My proposed index definition:
-id
-mktUSA
--mktId
--rating
--views
--price
...
-mktCanada
--mktId
--rating
--views
--price
...
-locales
--En
--Fr
--Zh
...
...other properties
The problem with this approach is configuring a magnitude scoring functions inside scoring profile, to boost products based on the market
Say eg: If user is from Canada, only the Canada based rating/views should be considered and not the other market ratings while Cognitive search is calculating the search relevance score.
Is there any possible work around this? Elastic search has a neat solution of Function score query that can be used to configure the scoring function dynamically
From what I understand, your problem is that you want to have a single index with products that support 109 different markets. Many different properties for your Product model can then be market-specific. Your concern is that the model gets to big, or if it's a scalable design. It is. You can have 1000+ properties without a problem.
I have built a similar search solution for e-commerce for multiple markets.
For price, I specify one price per market. I have about 80 or so markets, so that's 80 prices. There is no way around it. I would probably do the same for ratings and views too. One per market.
In our application we use separate dimensions for market, language and country. A market can be Scandinavia, BeNeLux or Asia-Pacific. You need to clearly define what a market is in your case, and agree with the business which markets you have and how you handle changes. Countries can map directly to markets, but it may also differ. Finally, language is usually shared across markets/countries and you usually only have to support 20-25 languages.
Suggested data model
Id
TitleEnGb
TitleDeDe
TitleFrFr
...
PriceGb
PriceUs
PriceNo
PriceDe
...
RatingsGb
RatingsUs
RatingsNo
RatingsDe
...
DescriptionEnGb
DescriptionDeDe
DescriptionFrFr
...
I try to illustrate that the Title and Description are language-specific. The price and ratings are market-specific.
For the 20-25 language-specific properties, you have to think about what analyzers to use. You want to use language-specific analyzers, and preferably the Microsoft analyzers since they have much better linguistics support with full lemmatization and so on.
When you develop your frontend application you have to keep track of which market, country and language you then refer to the specific properties. This is the easiest way to support boosting and so on.
Per-market index is not recommended
You could create one index per market. I have gone down this route before. I would not recommend this. This means you have to update 109 indexes every time you add, change or delete an item. And Azure Search supports 50 indexes per service at the most anyways.
I am interested in implementing a search feature on a website. It is a location search, so address/state/zip all should work. Which will then show results in that area and allow it to be filtered.
My question is:
What's the best approach for something like this?
There are literally dozens of ways of doing this (if not more). The exact implementation would depend on the technology stack that you use, but as a very top level overview:
you'd need to store the things you are searching for somewhere, and tag them with a lat/long location. Often, this would be in a database of some kind.
using a programming language, you would need to write a search that accepts a postcode, translates that to a lat/long and then searches the things in your database based on the distance between the location of the thing, and the location entered in the search.
if you want to support filtering, your search would need to support that too. This is often called "faceting" the search.
Working out the lat/long locations will need to be done using a GeoLocation service, there are some, such as PostCode Anywhere that will do this as a paid service, and others that are free (within reason), such as the Google Maps APIs.
There are probably some hosted services that will do what you want, you'd have to shop around.
Examples of search software that supports geolocation searching out of the box are things like Solr, Azure Search, Lucene and Elastic.
I've been using Spark Item Similarity through mahout by following the steps in this article:
https://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html
I was able to clean my data, setup a local-only spark/hadoop node and all that.
Now, my question relies more in the interpretation of the matrices. I've tried some Google queries with limited success.
I'm creating a multi-modal recommender - and one of my datasets is very similar to the Mahout example.
Example input:
Customer ActionName Product
11064612 view 241505
11086047 purchase 110915
11121878 view CERT_DL
11149030 purchase CERT_FS
11104130 view 111401
The output of mahout is 2 sets of matrices. A similarity matrix and a coocurrence matrix.
This is my similarity matrix (I assume mahout uses my "filter1" purchases)
**791207-WP** 791520-WP:11.350536461453885 791520:9.547158147208393 76130142:7.938639976084232 711215:7.0641921646893024 751309:6.805891904514283
So how would I interpret this? If someone purchased 791207-WP they could be interested in 791520-WP? (so I'd use the left part against purchases of a customer and rank products in the right part?).
The row for 791520-WP looks like this:
791520-WP 76151220:18.954662238247693 791604-WP:13.951210170984268
So, in theory, I'd recommend 76151220 to someone who bought 791520-WP, correct?
Part 2 of the question is interpreting the cross-similarity matrix. Remember my filter2 is "views".
How would I interpret this:
**790907** 76120956:14.2824428207241 791500-LXQ2:13.864741460885853 190907:10.735807818360627
I take this matrix as "someone who visited the 76120956 web page ended up purchasing 790907". So I should promote 790907 to customers who bought 76120956 and maybe even add a link between these 2 products on our site, for example.
Or is it "people who visited the webpage of 790907 ended up buying 76120956"?
My plan is not to use these as-is. I'll still use RowSimilarity and different sources to rank products - but I'm missing the basic interpretation of the outputs from mahout.
If you know of any documentation that clarifies this, that would be a great asset to have.
Thank you.
In both cases the matrix is telling you that the item-id key is similar to the listed items by the LLR value attached to each similar item. Similar in the sense that similar users purchased the items. In the second case it is saying that similar people viewed the items and this view also appears to have led of a purchase of the same item.
Cooccurrence works for purchases alone, cross-occurrence adds the check to make sure the view also correlated with a purchase. This allows you to use both for recommendations.
The output is meant to be used with a search engine generally and you would use a user's history of purchases and views as a 2 field query against the matrices, one in each field.
There are analogous methods to find item-based recommendations.
Better yet, use something like the Universal Recommender here: actionml.com/docs/ur with PredictionIO for an end-to-end system.
I want to use Azure ML to find related products using information from receipts from a store.
I got a file of reciepts:
44366,136778
79619,88975
78861,78864
53395,78129,78786,79295,79353,79406,79408,79417,85829,136712
32340,33973
31897,32905
32476,32697,33202,33344,33879,34237,34422,48175,55486,55490,55498
17800
32476,32697,33202,33344,33879,34237,34422,48175,55490,55497,55498,55503
47098
136974
85832
Each row represent one receipt and each number is a product id.
Given a product id I want to get a list of similar products, i.e. products that was bought together by other customers.
Can anyone point me in the right direction of how do to this?
This seems a good fit for their frequently bought together service (https://datamarket.azure.com/dataset/amla/mba). You may have to preprocess the dataset to get it in the required format. This service has a web UI as well: https://marketbasket.cloudapp.net/
This is a typical problem for Recommender, you can use a model called Machbox recommender to cover such a problem.
Recommender typically use Scoring about items to propose and the use some tricky calculation to predict scores for items users had not scored yet ( a score would be typically 1 user bought the item, 0 he did not)
If you need more details let me know ..(you have access to a free version of Azure ML where you can try all this)
Regards