Questions tagged [recommender-system]

Everything related to recommender systems

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4
votes
1answer
61 views

Can we use embeddings or latent vectors for a recommender system?

I'm having a hard time understanding why people use any vector they find as a candidate for a recommender system. In my mind, a recommender system requires a space where distance represents similarity....
3
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1answer
347 views

Use of negative correlation coefficient in pearson correlation algorithm for recommender systems

I am new to recommender systems and am trying to find similar users of base users for user-based collaborative filtering. When I calculated the similarity score between two users (based on their ...
0
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1answer
12 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 ...
1
vote
1answer
52 views

Recommendations based on other products seen

I am trying to develop a basic book recommender system to get in touch with the field and start learning methods and how to prepare the data. The Dataframe I am using is pretty plain, it has the ...
0
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0answers
9 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
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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 ...
0
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1answer
39 views

Document matching with more priority to certain features than others

I am working on recommendation systems wherein I need to match the similarity of 2 users. Now, I know that I can use Tfidf vectorizer to calculate the the cosine similarity score between them. But, ...
1
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0answers
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 , ...
1
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1answer
81 views

Initialize a recommender system with no dataset

Consider a platform for content recommendation based on the user history. The contents are books and articles and by history I mean what the user has read, what he has shared and so on. I know that ...
0
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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 ...
0
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2answers
26 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 ...
-1
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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 ...
2
votes
1answer
102 views

On the offline evalution of recommender system

There are mainly three ways to evaluate a recommender system: offline, online and user study. For most academic papers, offline evaluation is used to show the improvements: They split the offline ...
2
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0answers
45 views

What algorithm to choose order of university courses?

If I want to suggest a course path for a student who wanted to be a chemical engineering where each degree has to go through certain mandatory courses like math ,physics chemistry . Again to complete ...
0
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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
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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 ...
1
vote
1answer
54 views

Creating a Feature to determine popularity

I am building a recommendation system where I have multiple categories. I would like to Know how popular a product is in each category. For that, I am considering probability as one factor. For ...
4
votes
3answers
119 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 ...
0
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0answers
19 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
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0answers
11 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 ...
0
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2answers
73 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 ...
1
vote
1answer
44 views

How to model a supervised recommender system with varying data

Suppose there are 2000 movies and a company wants to recommend some movies (for example, at most 5 movies) to each visitor. The objective is to learn how to predict which movie will be selected if a ...
1
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0answers
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 ...
0
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0answers
38 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 ...
2
votes
1answer
248 views

How to use testing data set to measure recommender system algorithm

I am new to recommender systems and am trying to build one using item-to-time CF. Currently, I am trying to evaluate/measure results using MAE. I have one step which is unclear (after I managed to ...
0
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0answers
10 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 ...
1
vote
1answer
50 views

Recommendation Engine - Content based and Collaborative recommendation?

I am building a recommendation system for hotel accommodation. I scraped data from online booking portal and now my data has Name of the hotel, review, description and location. I built a simple ...
0
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0answers
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 ...
1
vote
1answer
44 views

Calculate Similarity using User's Personal Data?

I want to find out which users are similar to each other using their personal/organisational data, such as department, company, site, etc. I have this data in a boolean format, as shown below: ...
1
vote
1answer
29 views

Scoring metric for recommendation system

I'm working on a project that involves building a news recommendation system. I've come as far as quantifying user interaction with different articles on the site into user's affinity towards atopic ...
0
votes
1answer
76 views

When to stop showing content on recommendation engines?

Let's take an example. I log into my Netflix account and see that it's suggesting the show Friends to me. But I have no interest in watching Friends. So I ignore it. The next time I login, it suggests ...
2
votes
2answers
110 views

Matrix Factorisation Improvement

I am using SGD matrix factorisation (python) using the movielens dataset to make recommendations. I have a website which allows users to give feedback which is positive or negative to whether an item ...
4
votes
2answers
115 views

Evaluating recommendations quality and accuracy

I'm developing a recommendation system, that should provide my clients what actions they should take in order to hit certain targets. The underlying mechanics of the process is physical - where both ...
1
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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
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0answers
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 ...
2
votes
1answer
639 views

How do I perform Leave One Out Cross Validation For Top n Recommendation Sytems?

I am new in making recommendation systems . I am using the surpriselib library to evaluate my recommendations. All the Accuracy Metrics are well supported in this library. But I also want to compute ...
2
votes
2answers
90 views

How to recommend items after finding similar users in recommendation system

As the title explains my problem, I'm done with creating a recommendation system that can give me similar users for any given new user. The problem I face is, If I extract the list of products that ...
1
vote
1answer
26 views

How do I recommend items to out of training users based on its recent views?

I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings. Since we have a lot of users in system, I had to sample ...
1
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0answers
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 <...
2
votes
2answers
40 views

How to identify text similarity based on training data?

I have a set of documents (1 to 11) for which the labeling is done. Lets Assume: ...
1
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0answers
11 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
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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....
1
vote
2answers
142 views

Reduce data length to train effectively

I have customer buying data with each row specifying an item bought by customer. The problem is that even if at the same time customer buys five items then there are five different rows for it and as ...
1
vote
1answer
333 views

Can a recommendation system be used as a binary classifier?

I have a computer-generated music project, and I'd like to classify short passages of music as "good" or "bad" via machine learning. I won't have a large training set. I'll start by generating 500 ...
0
votes
1answer
26 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
vote
2answers
53 views

How do I correctly build model on given data to predict target parameter?

I have some dataset which contains different paramteres and data.head() looks like this Applied some preprocessing and performed Feature ranking - ...
1
vote
1answer
32 views

Call routing using AI

I'm trying to find out how AI can help with efficient customer service, in fact call routing to the right agent. My usecase is given context of a query from a customer and agents' expertise, how can ...
1
vote
0answers
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 ...
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 ...
3
votes
2answers
296 views

recommender systems : how to deal with items that change over time?

Let's say I am building a recommender system where items change through time. We suppose that each transaction is composed of : an item $i$ in list of items $(i_1, i_2, i_3, .., i_m)$. a user $u$ in ...

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