Questions tagged [recommender-system]

Everything related to recommender systems

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2
votes
1answer
585 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 ...
3
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2answers
278 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 ...
2
votes
1answer
1k views

cosine similarity between items (purchase data) and normalisation

I'm using IndexedRowMatrix which represents the products's user purchase behaviours and in order to build product recommendations, I use cosine similarity to calculate similarities between products. ...
1
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0answers
16 views

How do I store/model data needed for my recommendation module?

I'm reading data from a store's product catalog, a 100mb xml file which contains product-wise attributes like prices, categories, etc. Given a product_id, my job ...
0
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2answers
1k views

Does a matrix factorization recommendation engine use user/item related features?

All the tutorials I can find about matrix factorization recommendation systems start with importing users, items, and user-item-ratings, but then only use the rating matrix to train the recommender (...
4
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2answers
104 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 ...
4
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2answers
237 views

Recommender system for next career step

I want to build a recommender system that suggests the next step in your career. About the dataset. About 50'000 Users with following informations: Skills (tags, string value) every job they did (...
2
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1answer
433 views

Neural network model for sparse multi-class classifier on Tensorflow

The problem I'm trying to solve is the following: the data is Movielens with N_users=6041 and N_movies=3953, ~1 million ratings. For each user, a vector of size N_movies is defined, and the values ...
1
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1answer
72 views

Recommender system that connect users with each other , should I go for content based or collaborative filtering?

I am trying to build a system where user come on the platform and he chooses a topic(predefined few topics) and then we connect him with any random online user who chooses the same topic. Then they ...
2
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1answer
27 views

Building Recommender for book paragraphs

I have some application which are offering a book to read. Users normally read some paragraphs of it only (it contains +6000 paragraphs). Looking at scatter for ...
1
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1answer
73 views

Data model and algorithm for recommending “related” interests

On my app, when a user selects an interest (example: ios), I'd like to show related interests (swift, xcode, apple, etc). I have a list of around 700 interests/tags (about 300 of them can be ...
1
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1answer
26 views

Collaborative filtering with human-adjusted latent factors

Having tried some of movie recommendation engines available on the web I have the feeling they are not satisfactory. I just fail to get movies similar to those I like based on traits interested for me ...
2
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1answer
509 views

How to create user and item profile in an item to item collaborative filtering? (Non-rating case)

I want to build a recommender system for a coupons website which should do the following: Given the past purchase behaviour of a user, recommend coupons which the user is likely to buy. The data does ...
2
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0answers
237 views

SVD++ vs wALS: Which is the more effective for implicit feedback in Recommendation system

As SVD++ can be used for implicit feedback, I would like to know whether SVD++ can gives better results than the wALS algorithm (paper: Collaborative Filtering for Implicit Feedback Datasets ). I can'...
2
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2answers
164 views

Which recommender system: Content based or Collaborative filtering?

I want to build a recommender system for a coupons website which should do the following: Given the past purchase behaviour of a user, recommend coupons which the user is likely to buy. The data does ...
1
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1answer
31 views

Can I sum up feature vectors of a user‘s collection?

I want to find items that are similar to items users already have in their collection. Every item has attributes, so I created feature vectors where every element of the vector represents an attribute ...
3
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1answer
2k views

How to choose negative examples for recommendation system?

I am building a search recommendation system for e-commerce which generates most relevant results given an input query. I have framed it as a classification problem (learning to rank) and using ...
1
vote
1answer
79 views

What model can I build with a limited dataset?

I have a dataset consisting of purchasing history from an e-commerce website. The columns consist of customer id, product id, <...
0
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1answer
304 views

Problem in Recommendation for categorical data?

I have been building a recommendation model to recommend certain questions in an interaction platform to users to help each other. I have calculated an affinity score between categories to find which ...
1
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2answers
84 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 ...
2
votes
2answers
178 views

Taking Neural Network's false positives as the recommendation system result?

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 ...
2
votes
1answer
209 views

Two definitions of DCG measure

I wanted to check the definition of Discounted Cumulative Gain (DCG) measure in the original paper Jarvelin and it seems it differs from the one given in the later literature Wang. Originally, for $n$ ...
0
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1answer
340 views

Skills based recommendation system

Assuming that I have a list of Users with a list of skills: (each value is a different skill) And a list of Tasks with a list of demanded skills: Based on a manual classification that returned: (...
0
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1answer
32 views

How to implement a basic query management and recommendation system

I'm trying to prototype a system where given a textual query (e.g. a question), I get a list of most relevant documents/questions among a pool of available documents/questions (similar to what we see ...
0
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1answer
324 views

How to factorize the Matrix in TensorFlow? (Recommender System)

Given a user ratings matrix which is $n \times p$, where $n$ users rate $p$ movies, I already have a row matrix $n \times 10$ which characterises the user. I ideally wanted to use the TF was method ...
1
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1answer
2k views

How can we define missing rating in recommender system?

I was reading about collaborative filtering where we need to pass (user, item and rating) in case of matrix factorisation (SVD). Now, my question is given data of ...
3
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2answers
155 views

Learning to Rank Application

If there's a website/app that sells products and my job is to determine the order/ranking in which the products should be displayed. For example : I click on restaurants and a list of restaurants ...
0
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1answer
36 views

Is is possible to build a recommender system with this dataset? [closed]

I have an extremely sparse user items ratings matrix with 0.018 % non NA values. Correct me if I am wrong but I think we need a lot of products compared to number of users to build a recommender ...
1
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1answer
242 views

Predict ratings for Item Based Collaborative Filtering

Given the (cosine) similarity score of top 100 neighbors of every item, how do I predict ratings for unrated items? Please explain in simple terms. Item 1 260 0.577305 780 0.5655413 1210 0....
1
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1answer
414 views

Advantages of Binary Rating System for Collaborative Filtering Recommender Systems

I notice that Netflix, which I think used to use a five-star scale for rating content and give predicted ratings for unrated content on the same scale, now just has basic like/dislike buttons. Music ...
2
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1answer
65 views

How can I minimize features of the trainded model?

I have real technological process, that explained with complex model (xgboost). I.e. current mass of a product (y) depends on current temperature (x1), pressure (x2) and so on. I would like to solve ...
2
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1answer
275 views

How can I estimate user-item purchase probabilities of a e-commerce website?

I am writing my Master thesis, where the goal is to estimate user-item purchase probabilities. In other words, for a given user, what is the probability he/she will buy a certain item. I have session ...
1
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0answers
39 views

How to preprocess raw data in the form RDF triples to perform Information Gain and Principal Component Analysis? [closed]

I have my dataset in the form RDF triples in various domains such as Movie, music etc. Data is in the form of RDF triples (Subject, Property,Object (all uris). A sample input in the movie domain is ...
1
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1answer
515 views

What's the best classification model for this recommendation engine?

I'm not a data scientist but I'm trying to implement a recommendation engine on my company. My application runs on PHP but I'll use Python to process this data. My company is an online school, with ...
1
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0answers
34 views

What Algorithm to use for course path

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 ...
-2
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1answer
1k views

Best way to extract information from text description and match it with set of words

I have 10k records of data, each record represents a unique product(10k class labels) and its description. For example, "Coffee Maker, this product takes coffee beans and brew it, to make tasty cofe". ...
1
vote
1answer
416 views

Is this the correct way to apply a recommender system based on KNN and cosine similarity to predict continuous values?

My data is: userID, gameID, rating (1.0 through 10.0) First, I normalize the values the ratings of each row. I use cosine similarity to create a similarity matrix where each cell represents similarity ...
2
votes
2answers
108 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 ...
9
votes
3answers
548 views

Binary (Unary) Recommendation System with Biased Views

I would like to create a content recommendation system based on binary click data that also takes views into account. What content a user has been exposed to, and therefore has the chance to click ...
1
vote
2answers
75 views

Recommending college degrees based on high school subjects

I have a list of our college students' high school courses. I want to recommend college degrees to current high school students based on their courses - that is, predict a class based on a vector. ...
0
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1answer
32 views

Data normalisation and recommendation based on skillset

Given the job title return the skillset required for the job If a user is lacking some skills required for the job, we have to suggest the courses the user has to take to bridge the gap. Problem ...
1
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0answers
42 views

Multiple Seeds Content Based filtering Recommender System

I want to create a content based filtering recommender system which has multiple seeds. All that I have read about is having an initial seed from which the recommendations should be similar to. This ...
1
vote
1answer
2k views

benchmark Result for MovieLens dataset?

I am looking for a benchmark result or any kaggle competition held using MovieLens(20M or latest) dataset. Similar question has been asked here but, provided links are dead so re-raising the question.
2
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2answers
149 views

Is there any standard pattern recognition algorithm in predicting an item which a user will be buying next, given I have the history of the purchases

I am having a list of 10 different items a user has bought in the past. Each item has been bought multiple times. I would like to find a pattern in which the user buys a particular item and predict ...
5
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2answers
852 views

How to deal with position bias in search?

In search, position of the search result affects the click-through rate a great deal. How do people usually deal with this ? In practice how to remove such bias to create unbiased training data for ...
1
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0answers
23 views

Recommender system: Give a feature more significance than another

I am trying to build a recommender system that predicts hotel prices based on a great number of features. I have a column representing the hotel rating out of 5 and another column indicating the ...
2
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2answers
63 views

distinguish users for recommender system

How can you calculate better video recommendations for a SmartTV app which is used by multiple users in a household. I don't know which user is currently watching the video because the account for the ...
2
votes
1answer
297 views

How does a Recommender System recommend movies to a New User?

Consider a New user which has never rated any movie on the Website or the System has never seen the user. How does the System recommend Movies to the User and based on what ? How will we evaluate ...
2
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2answers
96 views

Predict the next player's option for betting based on playing history?

I have a project which is to predict in next time, which option player will select for betting. This prediction based on his history. For example, the player have time series data, the last options ...
1
vote
1answer
299 views

Which algorithm should be used for an accurate job recommendation system

I'm building a testing project to get an introduction to DS & ML. As a person part of the working force, sometimes finding a job is harder than it should be. I thought I could built a testing ...

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