Questions tagged [recommendation-engine]

For questions relating to recommendation engines, collaborative filtering, and personalization. Questions tend to be algorithmic or statistical in nature.

Recommendation engines are systems that suggest (recommend) to users various items for consideration. Some of the best known real world usage occurs at many major internet sites, such as Netflix, Amazon, Google, Yahoo, Pandora, last.fm, and others.

The input of such algorithms are most commonly the past purchases of the user, the rating the user gave to other goods, the items that have been purchased together ("customer who bought this also bought..."), the browsing history, etc.

For this tag, it is recommended that users focus on mathematical or statistical clarity, as it is a particularly advanced topic and implementation can take quite a bit of computational effort.

See also wikipedia.

1468 questions
15
votes
2 answers

Get Google Analytics "Visitors Flow" data from API

I'm trying to gather information from Google Analytics to build a recommendation engine for my site. The site consists of many pages, so I'm tracking the number of times a user clicks, for example, from page A to page B. Currently I can measure the…
14
votes
3 answers

Recommendation engine without ratings

I have found what must be dozens of articles on Towards Data Science/ medium/ etc. of people making recommendation engines with imdb data (based on ratings that users gave to movies, what movies should we recommend to those users). These articles…
amchugh89
  • 1,276
  • 1
  • 14
  • 33
14
votes
2 answers

Lightfm: handling user and item cold-start

I remember one of the strong points of lightfm is that the model does not suffer from cold start problem, both user and item cold start: lightfm original paper However, I still don't understand how to use lightfm to address the cold start problem.…
bohr
  • 631
  • 2
  • 9
  • 29
13
votes
1 answer

DatasetAutoFolds' object has no attribute 'global_mean' on python surprise

I'm using surprise to perform a cross validation def cross_v(data, folds=5): algorithms = (SVD, KNNBasic, KNNWithMeans, NormalPredictor) measures = ['RMSE', 'MAE'] for a in algorithms: data.split(folds); algo = a(); …
AFS
  • 1,433
  • 6
  • 28
  • 52
13
votes
4 answers

How to use mllib.recommendation if the user ids are string instead of contiguous integers?

I want to use Spark's mllib.recommendation library to build a prototype recommender system. However, the format of the user data I have is something of the following format: AB123XY45678 CD234WZ12345 EF345OOO1234 GH456XY98765 .... If I want to use…
shihpeng
  • 5,283
  • 6
  • 37
  • 63
12
votes
1 answer

Google Prediction API vs Graph Databases for Generated Recommendations?

(I admit I am no expert in graph databases or NoSQL, having only used it for a few hobby projects so far.) I've been using technologies like InfiniteGraph and Stig for recommendations - these are graph databases that supposedly are optimized for…
ina
  • 19,167
  • 39
  • 122
  • 201
12
votes
4 answers

Early stopping with multiple conditions

I am doing multi-class classification for a recommender system (item recommendations), and I'm currently training my network using sparse_categorical_crossentropy loss. Therefore, it is reasonable to perform EarlyStopping by monitoring my validation…
Marcus
  • 943
  • 5
  • 21
12
votes
3 answers

Writing a basic recommendation engine

I'm looking to write a basic recommendation engine that will take and store a list of numeric IDs (which relate to books), compare those to other users with a high volume of identical IDs and recommend additional books based on those finds. After a…
ndg
  • 2,585
  • 2
  • 33
  • 58
12
votes
2 answers

Neural Network Recommendation Engine

How would one design a neural network for the purpose of a recommendation engine. I assume each user would require their own network, but how would you design the inputs and the outputs for recommending an item in a database. Are there any good…
Louis
  • 4,172
  • 4
  • 45
  • 62
12
votes
2 answers

Mahout Plugin for ruby on rails

I want to use Apache Mahout in my project on Ruby on Rails for implementing recommendations and collaborative filtering. In Particular my requirements are: suggesting related tags. suggesting related articles. based on user's preferences prompt him…
12
votes
6 answers

Recommendation Engines for Java applications

I was wondering if there is any open source recommendation engine available? It should suggest something like Amazon and Netflix. I have heard of a framework called Apache Mahout - Taste. I am trying it next week. It would be great if you can share…
SomaSekhar
  • 348
  • 1
  • 6
  • 17
11
votes
4 answers

Multikey Multivalue Non Deterministic python dictionary

There is already a multi key dict in python and also a multivalued dict. I needed a python dictionary which is both: example: # probabilistically fetch any one of baloon, toy or car d['red','blue','green']== "baloon" or "car" or "toy" Probability…
10
votes
5 answers

How to implement a Digg-like algorithm?

How to implement a website with a recommendation system similar to stackoverflow/digg/reddit? I.e., users submit content and the website needs to calculate some sort of "hotness" according to how popular the item is. The flow is as follows: Users…
Niklas
  • 1,026
  • 2
  • 12
  • 19
10
votes
1 answer

Evaluating the LightFM Recommendation Model

I've been playing around with lightfm for quite some time and found it really useful to generate recommendations. However, there are two main questions that I would like to know. to evaluate the LightFM model in case where the rank of the…
10
votes
7 answers

What do I need in a database for "Customers Who Bought This Item Also Bought"?

Amazon has "Customers Who Bought This Item Also Bought". I am wondering and want to add this to my shopping cart which I made. What fields do I need in a database? Any website, blog or resources for this? Can you suggest the mechanism how to I…
shin
  • 31,901
  • 69
  • 184
  • 271
1
2
3
97 98