Questions tagged [recommender-system]

Everything related to recommender systems

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deploy content-based recommender system

The last couple of months I was working on a content-based recommender systems. Now it needs to be deployed. The content is not changing frequently, so I'm thinking about calculating the ...
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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 ...
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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 ...
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1answer
16 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 ...
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11 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 , ...
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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 ...
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1answer
20 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 ...
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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 ...
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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 ...
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3answers
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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 ...
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26 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 ...
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2answers
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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 ...
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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 ...
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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 ...
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27 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 ...
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1answer
37 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 ...
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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/...
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13 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 <...
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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 ...
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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....
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1answer
27 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 ...
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1answer
30 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 ...
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19 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 ...
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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 ...
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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 ...
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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?
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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 ...
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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 ...
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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 ...
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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. ...
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2answers
27 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 ...
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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 ...
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2answers
520 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 ...
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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 ...
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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 ...
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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 ...
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1answer
31 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 ...
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18 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 ...
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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 ...
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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: ...
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1answer
57 views

Two-tower net does not learn when made deep

I have been trying to train a relatively simple two-tower net for recommendation. I am using PyTorch and the implementation is the following - basically embeddings layers for users and items, optional ...
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1answer
22 views

Changing behaviour of an ML model

I am trying to create a ranking system for recommending books to an user. Let's suppose we have some subjects of books like 'A', 'B', 'C', 'D' and from the past behaviour, it is observed that the user ...
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1answer
23 views

Integer encoding and weighing when one feature consists of more names [closed]

Hello I am trying to make a content based movie recommendation system and one feature is genre of the movie. I will give an integer number to each genre randomly. However, some movies are of more than ...
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1answer
533 views

precision@k and recall@k

Normally, I am familiar with precision and recall evaluation metrics but as you know recall@k and precision@k are different things and used in ranking evaluations especially recommendation systems. I ...
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24 views

What is the right approach to bucket users for algorithms with different coverage for A/B testing

I've couple of recommendation algorithms that I want to A/B test. Algorithm A has 90% user coverage and algorithm B has 95% user coverage. That means if the algorithms are asked to provide ...
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58 views

LightFM implementation for scala/java on spark

I am looking for hybrid recommendation libraries such as lightfm that I can use on Spark (with Scala). Any alternative? Or best would be for me to build a hybrid recommendation system on spark's mllib ...
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0answers
57 views

Cross validation for Collaborative filter-based recommendation systems

I am an absolute beginner and am trying to implement collaborative filter for furniture ecommerce (think wayfair). I need some guidance about cross-validation strategy. Situation: I am working on a ...
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32 views

How does an autoencoder 'fill in the blanks' in the context of a recommender system?

My understanding is that an autoencoder takes an input, produces a lower dimensional representation of the input, which should explain the original features in the dataset, and then reconstructs the ...
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33 views

how to calculate the cosine similarity between two files?

I am using spark and scala to implement an issue. files contain phrases or sentences. I want to use domain based method to calculate the cosine similarity between tags.I convert two files into a ...
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1answer
72 views

Temporal train test split for recommender systems

When evaluating a collaborative filtering recommender system, it is practical to split the data temporally. However, by doing so, some users might be present in only either of the train or test set. ...

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