Questions tagged [recommender-system]

Everything related to recommender systems

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1answer
85 views

What ML algorithm can I use for building a “recommended” list for players?

We have a large touchscreen kiosk in local malls where people can go up to it and play a game under different categories. We want to implement ML to build a recommended list of games for each player. ...
2
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1answer
196 views

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

I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering. When I calculated the similarity score now between two users (based on there ...
7
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2answers
4k views

Recommender system based on purchase history, not ratings

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account i) product holdings ii) user characteristics (segment, age, affluence, etc.). I ...
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1answer
56 views

Searching documents at scale: how to maintain cleaned documents?

I have a document-store database (MarkLogic) with hundreds of thousands of news articles in raw format. I am building a content recommender on a representative subset of that data on my local machine. ...
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1answer
2k views

Updating One-Hot Encoding to account for new categories

My question is focused around how to appropriately update an encoded feature set when a new category is introduced by the test data. I use the data in logistic regression and I know it is not a 'live' ...
0
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1answer
291 views

Matching content item to a persons profile

I'm working on an experiment which is essentially a content recommendation service. I have a set of content items in the form of articles, tweets, blog posts etc which have had a set of tags ...
3
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3answers
280 views

Item Similarity with Location Feature

I'm currently learning about Collaborative Learning and Content-based Recommendation. One of the main things that is discussed in both methods is about calculating similarity between two users or two ...
2
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2answers
129 views

Proceeding with various methods for news recommendation

I am beginner in ML (i have done only Andrew Ng's ML course) and i have to work on news recommendation. I went through this paper which mentions different methods used for news recommendation (at 7th ...
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2answers
77 views

New to Data Science - What to use when looking for a pattern/relationship between items and an outcome

I am new to data science and I am hoping I can start applying it to my job. I have watched some videos on places like Udemy for Machine Learning and Python etc. Anyway, I have a task but I am not ...
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0answers
574 views

Recommender system based on binary likes/disklikes?

I am building a recommender system. I have a list that shows me what a user has disliked and I use it to create a dataset. The dataset shows me: ...
3
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1answer
90 views

Neural Network - Sparsity of collaborative based filtering and modelling the prediction problem

I'm fairly new to machine learning and for that matter, neural networks, but for the past couple of days I decided to take a stab at a fairly classical and practical problem of neural networks/machine ...
3
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1answer
284 views

Interpretation of an SVD for recommender systems

The idea is to motivate the SVD for use in a recommender system. Consider a matrix $A\in \mathbb{R}^{f\times u}$ where $A_{ij}$ caputures how user $j$ rates film $i$ (on a scale from 1-10, some ...
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0answers
294 views

Recommendation matrix as a product of User Similarity and Ratings

For both item-item and user-user collaborative filtering the recommendation matrix $Γ_{m x n}$, which is an (m x n) matrix, can be defined as: $$Γ(i,j)=r_{ij}$$ For user-user collaborative ...
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1answer
774 views

What algorithms should I choose for a recommender system and why?

To my knowledge, recommender systems are broadly classified into collaborative and content. Collaborative in turn is divided into 1) Memory (uses similarity metrics) and 2) Model (well known Matrix/...
2
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1answer
4k views

Predict customer action from previous buying history

I'm trying to predict what service a customer wants when he comes to our office from his previous transactions history. I have 7 years transactions data(3 crore txns) and good amount of customers are ...
0
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1answer
40 views

In Recommendation systems, Does we need to build each model for each product if we are using Logistic regression?

I am reading this paper wide and deep learning recommedation system paper and haven't understood one thing. To serve the latency, they actually first get the 100 best apps according to user query ...
2
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1answer
147 views

Translating a business problem into a machine learning solution: job-adds website

I have the following problem: A company, let's call it X, has a job-adds website. It works as a marketplace where job-seekers and companies that have job vacancies can meet. Their business model is ...
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2answers
632 views

Interpret results from a lightFM factorization machines

I built a recommendation model on a user-item transactional dataset where each transaction is represented by 1: model = LightFM(learning_rate=0.05, loss='warp') ...
1
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1answer
103 views

Recommender System: how to treat different events

I'm trying to build recommender based on user history from e-commerce. There are two(potentially more) types of events: purchase and view. Is it okay to sum up number of purchases and views for a ...
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0answers
37 views

Books/reviews/papers on recommending groups of items?

Looking for books (chapters?)/reviews/papers on the task of recommending a few (possibly non-independent) items. Example: office supply shop recommending a person a pre-compiled package of items of ...
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0answers
579 views

Feeding data to Xgboost for recomender system

I am using xgboost for a recommender system. There are 3-4 recommended content on each page. My data consists of columns like page_id and advertisement_id. Currently for every page_id, there are 3-4 ...
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1answer
96 views

Supervised Recommendation System trained on labeled phrase segments

I have a big collection of phrase segments (not whole ones) with user provided labels based on text similarity: ...
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1answer
151 views

POC - Get an idea to create a Predictive Model

I'm trying to look for an idea to create a predictive model having the following data: ...
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1answer
2k views

Recommendation System to integrate with an android app [closed]

I need to build a recommendation system that takes certain parameters as input, computes a score and order suggestions to users based on this score. Well this is what I need to do loosely speaking. I ...
2
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2answers
736 views

Job Recommendation Engine

My girlfriend has recently been struggling with finding a new job, so I thought I'd make a website to help her out. The basic idea is that she'll be shown a list of jobs, rate her interest, and then a ...
4
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2answers
3k views

SVD for recommendation engine

I'm trying to build a toy recommendation engine to wrap my mind around Singular Value Decomposition (SVD). I've read enough content to understand the motivations and intuition behind the actual ...
2
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2answers
206 views

Which recommender system approach allows for inclusion of user profile?

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage ...
4
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1answer
163 views

Recommendation/personalization algorithm conflict

I'm trying to build a recommendation engine for an e-commerce site. By using the common recommendation approach, I'm assuming that each product I recommend has the same value, so all I need to do is ...
3
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1answer
369 views

How to handle data collecting bias in machine model training

In many ML problems we collect data and train models using the collected data. Using recommendation as an example, data collected could be biased for various reasons: presentation bias. For example, ...
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5answers
3k views

Machine learning algorithm for ranking

I am working on a ranking question, recommending k out of m items to the users. The evaluation metric is average precision at K. Both R and Python have xgboost can be used for pairwise comparison ...
2
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2answers
4k views

spark item similarity recommendation

I would like to build an recommendation engine using spark's Mlib itemsimilarity as mentioned here LINK But it seems spark do not have this algorithm any more and ...
10
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1answer
3k views

Spark ALS: recommending for new users

The question How do I predict the rating for a new user in an ALS model trained in Spark? (New = not seen during training time) The problem I'm following the official Spark ALS tutorial here: ...
2
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1answer
491 views

Which algorithms should I use for recommendation system using a graph database?

Basically I'm developing a recommendation system using a graph database (specifically neo4j), and I want to apply recommendation algorithms. Since i'm using a graph database, I can see the ...
2
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1answer
742 views

how to evaluate top n recommendation system with movie lens dataset?

Based on my research a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And I'm currently ...
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4answers
246 views

How can conclusions be drawn from recommendation systems evaluation?

From my research, a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And basically exists many ...
3
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1answer
547 views

Using AWS ML to recommend products

I have millions of user ratings on about 2k products. I want to use Machine Learning to analyse these ratings and recommend products to users based on other users ratings of the same and different ...
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3answers
857 views

How to use ML to forecast sales of a brand new product

I am working on a forecasting problem and came across this issue. How do I forecast sales of a brand new product? For example, a product has been introduced in the store and the store would like to ...
2
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0answers
305 views

wrong prediction from graphlab.recommender.item_similarity_recommender

I have a question about basic understanding of how item-item collaborative filtering of "Graphlab" library works. I run this code: ...
3
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3answers
727 views

Solution for in Time/Space Complexity challenge in Recommendation System?

I have a book Recommendation System project and have a huge data set of feature vectors. What is the best solution for in memory computation? I mean, the program should: calculate the cosine ...
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0answers
191 views

KNN on collaborative filtering

After I calculated the similarities matrix, how do I get the neighbors, for example, consider the matrix of similarities between users, if I did not make any mistakes, the matrix must be symmetric ...
2
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2answers
6k views

Clustering users based on buying behaviour

I have a set of data which indicates purchase transaction of users (~1 million records). User can have more than 1 purchase across time. Data is spread over 6-7 months. Attributes that I have are ...
2
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1answer
433 views

recommendation system for eCommerce healthcare portal suggestion

I am trying to build a recommendation system. My system is basically a ecommerce application where our customers answers a bunch of questions related to healthcare (their basic health related question)...
2
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0answers
320 views

Taxonomy of recommender system methodologies

There's tons of material online but yet I can't reconcile the different definitions for recommender system methodologies / strategies. I think we can identify several axes: memory vs model based; ...
4
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1answer
911 views

Recommendations and Missing Data in Deep Learning

In this research paper, it is discussed how to combine deep learning with wide (shallow) learning to achieve both generalisation and the ability to learn correlation/association rules. The input ...
1
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1answer
2k views

Multiclass Classification with large number of categories

I am making a recommendation system (kind of) and I have to recommend the item a user is most likely to buy in his next purchase. Doesn't matter if he already bought this item. Given this, I'm ...
2
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1answer
277 views

Vectorizing equation in MATLAB

I am working on collaborative filtering using matrix factorization in MATLAB. I am using Gradient Descent for parameter learning. The cost function to optimize is : $ J = {\left\| I \odot (R - U V') \...
7
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2answers
3k views

Which supervised learning algorithms are available for matching?

I'm working on a non-profit where we try to help potential university applicants by matching them with alumni that want to share their experience/wisdom and, at the moment, it is happening manually. ...
8
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2answers
250 views

What should be the value of non-rated field when finding cosine similarity

I am working on a very basic book recommender system. I want to know what to do with the fields which aren't rated by the user when finding cosine similarity, should we ignore them and calculate only ...
1
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1answer
146 views

Match users based on the content of their articles

I have users in my database that I would like to match up or group togetter based on the content of there articles. I cant seem to find how this kind of problem is being solved today. Any advice will ...
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0answers
483 views

Spark MLlib recommendation - restaurant/ item similarity - issues/improvement

Use Case Recommend similar items (restaurants) to diner. Solution: We have used apache spark MLlib ALS algorithm. Values for lambda and rank has been obtained by iterating through all permutations ...