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.
Related
I'm wondering if there is a way (specific package, process, etc.) of grouping items based on an overall category? For example, I'm looking at empty search results and want to see what category customers are most interested in.
Let's say I have a list of searched terms: skittles, laundry, snickers and detergent. I would want to group these items based on a broader category (i.e., skittles and snickers are "candy" and laundry and detergent would be "cleaners").
I've done some research on this and have seen similar (but not exact) ways of doing this (e.g., common keyword grouping using NLP) but not sure if something like this exists in the world when there isn't necessarily any commonality. Any help or direction would be greatly appreciated.
Update here: The best way to handle this scenario is to use pretrained word embeddings using something like Google's BERT algorithm as the first pass and then layer on another ML model that is specific to the use case.
Case in point: say we have a search query that returns 2000 results ranging from very relevant to hardly relevant at all. When this is sorted by relevance this is fine, as the most relevant results are listed on the first page.
However, when sorting by another field (e.g. user rating) the results on the first page are full of hardly-relevant results, which is a problem for our client. Somehow we need to only show the 'relevant' results with highest ratings.
I can only think of a few solutions, all of which have problems:
1 - Filter out listings on Solr side if relevancy score is under a threshold. I'm not sure how to do this, and from what I've read this isn't a good idea anyway. e.g. If a result returns only 10 listings I would want to display them all instead of filter any out. It seems impossible to determine a threshold that would work across the board. If anyone can show me otherwise please show me how!
2 - Filter out listings on the application side based on score. This I can do without a problem, except that now I can't implement pagination, because I have no way to determine the total number of filtered results without returning the whole set, which would affect performance/bandwidth etc... Also has same problems of the first point.
3 - Create a sort of 'combined' sort that aggregates a score between relevancy and user rating, which the results will then be sorted on. Firstly I'm not sure if this is even possible, and secondly it would be weird for the user if the results aren't actually listed in order of rating.
How has this been solved before? I'm open to any ideas!
Thanks
If they're not relevant, they should be excluded from the result set. Since you want to order by a dedicated field (i.e. user rating), you'll have to tweak how you decide which documents to include in the result at all.
In any case you'll have to define "what is relevant enough", since scores aren't really comparable between queries and doesn't say anything about "this was xyz relevant!".
You'll have to decide why those documents that are included aren't relevant and exclude them based on that criteria, and then either use the review score as a way to boost them further up (if you want the search to appear organic / by relevance). Otherwise you can just exclude them and sort by user score. But remember that user score, as an experience for the user, is usually a harder problem to make relevant than just order by the average of the votes.
Usually the client can choose different ordering options, by relevance or ratings for example. But you are right that ordering by rating is probably not useful enough. What you could do is take into account the rating in the relevance scoring. For example, by multiplying an "organic" score with a rating transformed as a small boost. In Solr you could do this with Function Queries. It is not hard science, and some magic is involved. Much is common sense. And it requires some very good evaluation and testing to see what works best.
Alternatively, if you do not want to treat it as a retrieval problem, you can apply faceting and let users do filtering of the results by rating. Let users help themselves. But I can imagine this does not work in all domains.
Engineers can define what relevancy is. Content similarity scoring is not only what constitutes relevancy. Many Information Retrieval researchers and engineers agree that contextual information should be used besides only the content similarity. This opens a plethora of possibilities to define a retrieval model. For example, what has become popular are Learning to Rank (LTR) approaches where different features are learnt from search logs to deliver more relevant documents to users given their user profiles and prior search behavior. Solr offers this as module.
I have 20,000 messages (combination of email and live chat) between my customer and my support staff. I also have a knowledge base for my product.
Often times, the questions customers ask are quite simple and my support staff simply point them to the right knowledge base article.
What I would like to do, in order to save my support staff time, is to show my staff a list of articles that may likely be relevant based on the initial user's support request. This way they can just copy and paste the link to the help article instead of loading up the knowledge base and searching for the article manually.
I'm wondering what solutions I should investigate.
My current line of thinking is to run analysis on existing data and use a text classification approach:
For each message, see if there is a response with a link to a how-to article
If Yes, extract key phrases (microsoft cognitive services)
TF-IDF?
Treat each how-to as a 'classification' that belongs to sets of key phrases
Use some supervised machine learning, support vector machines maybe to predict which 'classification, aka how-to article' belongs to key phrase determined from a new support ticket.
Feed new responses back into the set to make the system smarter.
Not sure if I'm over complicating things. Any advice on how this is done would be appreciated.
PS: naive approach of just dumping 'key phrases' into search query of our knowledge base yielded poor results since the content of the help article is often different than how a person phrases their question in an email or live chat.
A simple classifier along the lines of a "spam" classifier might work, except that each FAQ would be a feature as opposed to a single feature classifier of spam, not-spam.
Most spam-classifiers start-off with a dictionary of words/phrases. You already have a start on this with your naive approach. However, unlike your approach a spam classifier does much more than a text search. Essentially, in a spam classifier, each word in the customer's email is given a weight and the sum of weights indicates if the message is spam or not-spam. Now, extend this to as many features as FAQs. That is, features like: FAQ1 or not-FAQ1, FAQ2 or not-FAQ2, etc.
Since your support people can easily identify which of the FAQs an e-mail requires then using a supervised learning algorithm would be appropriate. To reduce the impact of any miss-classification errors, then consider the application presenting a support person with the customer's email followed by the computer generated response and all the support person would have to-do is approve the response or modify it. Modifying a response should result in a new entry in the training set.
Support Vector Machines are one method to implement machine learning. However, you are probably suggesting this solution way too early in the process of first identifying the problem and then getting a simple method to work, as well as possible, before using more sophisticated methods. After all, if a multi-feature spam classifier works why invest more time and money in something else that also works?
Finally, depending on your system this is something I would like to work-on.
This question is probably very repeated in the blogging and Q&A websites but I couldn't find any concrete answer yet.
I am trying to build a recommendation system for customers using only their purchase history.
Let's say my application has n products.
Compute item similarities for all the n products based on their attributes (like country, type, price)
When user needs recommendation - loop the previously purchased products p for user u and fetch the similar products (similarity is done in the previous step)
If am right we call this as content-based recommendation as opposed to collaborative filtering since it doesn't involve co-occurrence of items or user preferences to an item.
My problem is multi-fold:
Is there any existing scalable ML platform that addresses contend based recommendation (I am fine to adopt different technologies/language)
Is there a way to tweak Mahout to get this result?
Is classification a way to handle content based recommendation?
Is it something that a graph database good at solving?
Note: I looked at Mahout (since am familiar with Java and Mahout apparently utilizes Hadoop for distributed processing) for doing this in scale and advantage of having a well tested ML algorithms.
Your help is appreciated. Any examples would be really great. Thanks.
The so called item-item recommenders are natural candidates for precomputing the similarities, because the attributes of the items rarely change. I would suggest you precompute the item similarity between each item, and perhaps store the top K for each item, and if you have enough resources you could load the similarity matix into main memory for real time recommendation.
Check out my answer to this question for a way to do this in Mahout: Does Mahout provide a way to determine similarity between content (for content-based recommendations)?
The example is how to compute the textual similarity between the items, and than load the precomputed values into main memory.
For performance comparison about different data structures to hold the values check out this question: Mahout precomputed Item-item similarity - slow recommendation
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