Questions tagged [recommender-system]
Everything related to recommender systems
411
questions
1
vote
1answer
26 views
How to choose embedding size for tensorflow recommender system
I am going to build a recommender system using TensorFlow recommender and the two-tower-model. I have wondered, how to choose the size of the embedding dimension. Are there any papers on this for ...
0
votes
0answers
22 views
The best ML algorithm to give recommendations to fill in a selection
I have a ML problem where I want to suggest a combination of options to the user based on their current choice of options. The options are all boolean (selected or not selected), there are several ...
0
votes
0answers
10 views
Test dataset contains invalid data. ( Error 0018 ) in Azure ML Studio Evaluate Recommender
I am doing a crop recommender system using the Matchbox recommender system in Azure ml studio.
while splitting the dataset using Recommender split, it won't be split. but I split while using split ...
1
vote
0answers
8 views
Building crop recommender system with past cultivation data not with ratings
I am planning to create a Crop recommendation system for farmers using the past ten years' crop cultivation data. it is a mobile application. whenever a farmer selects his location, the system will ...
0
votes
0answers
18 views
Recsim for Movie recommendation
I was reading through Recsim
Is there a simple implementation of this - On MovieLens data for example?
My understanding so far -
The UserModel is for modelling and sampling users - based on user ...
0
votes
0answers
15 views
Mutiple binary classification for for best propensity to buy one of the product
Problem:- I have 5 products for sell and I can pitch only one product in a month to one customer.so I wants to know which product customer can buy.
Proposed solution:-
I build 5 binary logistic models ...
0
votes
0answers
10 views
Suggestions re best approach for the task: recommender systems, contextual bandits, classification,
The task is about choosing the best compensation option for a customer. A customer (features: regular/premium, old/new, etc ...) which is facing a certain type of an issue such as delayed order (...
0
votes
0answers
18 views
Tensorflow Keras predicting one-hot-vectors
I am trying to build a recommender predicting products. The inputs are one-hot encoded product IDs, and so is the output. (0, 0, 1, 0) to (1, 0, 0, 0)
There are 83 unique IDs in the first category and ...
1
vote
2answers
42 views
What methods exist to handle non politically correct recommendations?
Every now and then we hear about another ML-based recommendation system that suggested a politically offensive result to users. What methods are currently in use to prevent such cases in modern ...
0
votes
0answers
22 views
Ranking recommendation system
I have problem with construction recommended system. I have a DataFrame with columns of users , items (books) and the order in users read the book -
...
0
votes
0answers
15 views
Instagram Profile Similarity Features
I want to find similar IG accounts in a semantic way(not demografic like fan count, language, country,...) and thought of the following features:
Post Text Similarity (Embeddings by SBERT, averaging ...
0
votes
1answer
14 views
Do I need to read an entire database for a recommendation system?
Let's say I have a database with approx 100000 rows. I want to build a content-based recommendation system. Do I really need to read the entire database to calculate similarity? That would be very ...
0
votes
0answers
11 views
How to explain rescaled recommendation scores to end-user?
I constructed a recommendation system based on both Boolean and linear features that present products to potential buyers. The raw scores are not easily digestible for humans (i.e. the highest score ...
0
votes
1answer
17 views
Any well documented algorithm/function for previously bought recommendation system
I'm working on a previously bought recommendation system for a project. The list I'm trying to sort is static and does not change over time. Assuming each user purchases different items at different ...
1
vote
0answers
13 views
Using Position as Feature in offline learn to rank
I have been wondering about a particular technique to denoise position bias in learn-to-rank.I am aware of inverse propensity weighting techniques. During a discussion, it was suggested to me , ...
0
votes
0answers
10 views
Building a recommendation system with a graph database
When I'm reading about building recommendation systems with collaborative filtering and they generally don't talk about graph databases like neo4j. Are graph databases enough to implement the best ...
1
vote
1answer
22 views
RecSys - Large dataset, few resources, how to sample?
I have been working with a RecSys model, for the first time, by experimenting with matrix factorization and matrix factorization with EmbedNN's. However, I am running into a memory problem since my ...
0
votes
0answers
23 views
What are good resources to learn KNN text classification using Tensorflow?
I found the KNN image classification tutorial using MNIST dataset and was able to lear how it's working.
Are there any resources that describe to apply the same KNN algorithm to text classification. I ...
0
votes
0answers
39 views
How to incorporate multi-task in CTR/recommendation model (deep & wide/ xDeepFM etc)?
I am building a rank algorithm for an e-commerce website that ranks the product based on likely hood of purchase and I have formulated this problem into a binary classification problem. Given each ...
4
votes
3answers
180 views
Treating recommender systems as multiclass classification or binary classification problem
I'm thinking about the two following approaches for building a recommender system to recommend products using implicit data as a classifier:
Treat it as a multi-class classification problem. The ...
1
vote
1answer
29 views
Reommender system model Predicting the time watched duration for each user_id-video_id pair
I just want to ask If I can use Surprise Library (SVD algorithm) in building a recommender system that predicts the watch duration for a user_id and video_id pair?
I have a dataset that contains the ...
0
votes
2answers
107 views
Advantages of matrix factorization when the number of products is low
I'm building a recommender system where the number of products is rather low (around 50), and we can assume it'll stay the same for a long time.
I'm looking at two different way of tackling the ...
0
votes
0answers
40 views
Recommender System with Time as the dependent variable and not ratings
I'm currently designing a recommender system in watching videos (all with same duration). I have the user_id and the video_id and I have the data for users's watch duration for the video_id.
I ...
0
votes
0answers
11 views
Recommender system for matching user input keywords to objects that have different keywords assigned to them (and getting the matching weights)
I'm looking for some tips in the right direction as to what to look into for this recommender system:
We have a predefined set of objects, each with a few keywords assigned to them. We can call the ...
0
votes
0answers
33 views
How to use Tensorflow Recommenders' Retrieval task with Keras data generators
I've recently started working with the package to build recommender systems, and so far, I've successfully built a Ranking task that takes the inputs from a Keras Data Generator. However, I could not ...
0
votes
1answer
46 views
Change feature importance in a trained model
I am giving a toy example for describing a real world business problem. Let's say I am a publisher and I have some book stores to visit. By visiting those stores I will check whether they have ...
1
vote
0answers
9 views
Searching for movie dataset containing movie synopses? [closed]
To build a hybrid recommendation system, I used the movielens 1M dataset, for the collaborative filtering part. Now, I'm looking for a database/dataset that contains descriptions/summaries/details/...
1
vote
0answers
22 views
Dot product of two matrices in NLP how can i get this error be solved [closed]
from sklearn.metrics.pairwise import linear_kernel
sim_matrix = linear_kernel(tfidf_matrix, tfidf_matrix)
when I try to get dot product I am getting this errro
<...
1
vote
0answers
15 views
Recommendation System with ALS Implicit
I created a model for Recommending top 10 items to users similar to the approach used here https://github.com/benfred/implicit/blob/master/examples/lastfm.py
I wanted to evaluate the model using NDCG ...
1
vote
0answers
12 views
is it better to use embedding with attributes in collaborative filtering or content-based approach?
I have a dataset with different text documents, a set of users who each read a different document, some historical info such as their reading speed, and other attributes related to the texts and users....
0
votes
1answer
33 views
Custom POS tagger for health issues [closed]
I am new to NLP, I have a bunch of raw data that is not tagged at all of medical questions, I need to extract from them what are the health issues stated in those texts.
I was thinking I need to ...
-1
votes
1answer
33 views
Is it possible to implement a Recommender System without having a ratings/previous purchases similar data?
I'm trying to implement a recommender system for a website that hosts a wide variety of software and you can search the website to find what you need. The need is to implement a recommender system to ...
1
vote
0answers
20 views
Recommender System Approaches
I have a 4 datasets with user features, item features, user-item rating and User-item link data. I'm trying to build a recommender system to recommend top 10 items to the user by maximizing NDCG as ...
0
votes
0answers
36 views
AB testing for Recommender models
Let's say that I have two recommendation system models built, Model A and Model B.
Now I track the performance of both the models for 5 days from 1st Jan to 5th Jan.
Each model has been assigned a ...
0
votes
0answers
12 views
How to ensure diversity in my recommended ranking?
I have generated a ranked list of items but I want to ensure that the ranking takes care of diversity basis some item metadata. Most of the way I can think of seems computationally expensive. Is there ...
0
votes
0answers
11 views
If we only have the rating scores of items provided by the users, how do we use matrix factorization to build a recommender system model?
If we only have the rating scores of items provided by the users, how do we use matrix factorization (MF), factorization machine (FM), and deep learning (DL) to build a recommender system model?
0
votes
1answer
30 views
how to calculate similarity between users based on movie ratings
Hi I am working on a movie recommendation system and I have to find alikeness between the main user and other users. For example, the main user watched 3 specific movies and rated them as 8,5,7. A ...
0
votes
0answers
11 views
Choosing the size of the network for Neural Collaborative Filtering (NCF)?
I've been working on Neural Collaborative Filtering (NCF) recently to build a recommender system. After doing some hyperparameter tuning with various sizes for embedding and dense layers sizes, from ...
0
votes
0answers
9 views
how to evaluate the performance of a recommender system with single recommendation
Say we have a recommender system in production which recommends 1 our of N items according to some internal algorithm f given inputs Xi for each user i, let's assume f is a black box model. We have ...
2
votes
0answers
26 views
Best way to evaluate interlaced recommendation system results while reducing bias
I already asked this question but I worded it in such a way that it was a completely different question to the one I want to ask. I have not deleted the old question in case someone finds it useful.
...
0
votes
2answers
46 views
Best approach for A/B testing two different recommendation systems
I have two recommendation systems for musical preference that make a list of predictions for a particular user based on the songs they have saved in their library. The user then rates how good each ...
0
votes
0answers
12 views
How does Latitude and Longitude be helpful in making the Venues/Places Recommendation system?
I am trying to build a recommendation system which suggest the places on the basis of their ratings , reviews etc . I want to use Latitude and Longitude , but I don't know how it will be helpful in ...
4
votes
2answers
583 views
What is difference between Nearest Neighbor and KNN?
I was taking the tutorial of making Recommendation system , there I read that Nearest Neighbor is different from KNN classifier . Could anyone explain that what is Nearest Neighbor and how it is ...
0
votes
0answers
37 views
I need direction for a research project
I am new to machine learning so please bare with me. I'll try to keep this short and sweet.
We are building a makeup simulation and recommendation system. My part is to recommend a makeup which is ...
0
votes
0answers
10 views
Methods to generalise NCF recommender systems to unseen users, same set of items?
I'm new to recommendation models, and am starting to build a recommender system on the MovieLens dataset using NCF-style model.
As I'm building it I'm wondering if, once trained, I can apply it to my ...
1
vote
0answers
18 views
How to get KNN linearly hybridised by two similarities?
I'm writing a KNN (collaborative filtering) hybrid similarity recommender and I need some advice. It is based on this paper.
I've currently got 2 datasets. The first one is ...
0
votes
1answer
34 views
Cold start recommender system with features
I have to develop a recommender system where most of the users only buy 1 item, so I have a cold-start problem. For this reason, I'm discarding matrix factorization techniques and content-based ...
0
votes
0answers
19 views
How metric learning works for content based item retrieval
I was doing some computer vision experiments and recently I have started learning about metric learning and the image retrieval problem. I was experimenting with the inshop image retrieval dataset to ...
0
votes
0answers
8 views
Model performance in different snapshots varying
I am trying to solve this problem. A medical representative needs to visit some doctors' clinics and for that a model will generate probability scores for visiting a clinic. I ma using a tree based ...
0
votes
1answer
27 views
Calculate implicit rating from streaming behaviour for Recommendation Engine
I have a dataset containing some user streams data for particular videos like below:
...
