4 votes

Can recommendation systems be created for other data other than images?

Recommendation systems can be applied for anything, as long as you have sufficient training data. The most important inputs to the recommendation system are not "audio files or video files". ...
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  • 1,390
3 votes
Accepted

What is the most appropriate ML algorithm for creating recommendations

You can use Collaborative Filtering, and specifically its memory based approach. The problem that you have discussed in the question should probably be solved using ...
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3 votes
Accepted

How do recommendation systems work?

Let me try to explain how recommender systems work in production, as intuitively as possible: Let's say we want to build a rec sys. for a restaurant discovery product, where users can rate ...
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  • 1,351
2 votes

Cold start collaborative filtering with NLP

From what I understood you will not have any cold start problem because you basically process the user preferences description against movies descriptions to get recommendations. So you don't use ...
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  • 409
2 votes
Accepted

Which reward function works for recommendation systems using knowledge graphs?

I found the answer further into the paper (section 3.2 Formulation as Markov Decision Process)! I'll post it here for everyone. Given any user, there is no pre-known targeted item in the KGRE-Rec ...
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2 votes

How do I plot a matrix of ratings?

You're looking for a heatmap. Check out e.g. https://stackoverflow.com/q/33282368/3924118 (if you like Python more than the others). See also this documentation.
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  • 34.4k
1 vote

A recommender system based on millions of fields including text and number

The first step You need to decide if you want to hold each string column or not. Then you must encode your text fields into numbers which you need to use some embedding algorithms like word2Vec. Check ...
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  • 111
1 vote
Accepted

What is meant by the rank of the scoring function here?

$\text{rank}(f((r,e)|u))$ in $A_t(u)$ means to compute the value of scoring function $f$ for all pairs $(r,e)\in A_t$ which are conditioned by $u$, then sort them in a descending order. The rank of ...
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  • 1,663
1 vote
Accepted

Why can't pure KG embedding methods discover multi-hop relations paths?

To put this insert to context, we should take at least this much of text from the paper: One line of research focuses on making recommendations using knowledge graph embedding models, such as ...
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  • 927
1 vote
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Which model is better given their training and validation errors?

I would say that your intuition is correct: the model associated with the first plot is likely to generalise more than the one associated with the second plot. In both cases, it doesn't seem that ...
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  • 34.4k
1 vote
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What are multi-hop relational paths?

Before trying to explain this term in your context, let me briefly describe the term in other contexts. In computer networking, the term "hop" refers to a node (e.g. a router) that a packet goes ...
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  • 34.4k
1 vote

Using AI to enhance customer service

AI can transform the customer experience is by providing personalized content. For example, When you see video recommendation on YouTube, you'll know that it's from AI technology. I recommend you to ...
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1 vote

Using AI to enhance customer service

This sounds to me like a use case for a chatbot. You would have different intents reflecting the types of user queries that your system can respond to. The intent matching can be done by pattern ...
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  • 5,062
1 vote

How to get top 5 movies recommendations from Auto-Encoder

That is not what an auto-encoder is doing. An auto-encoder gives you a compressed representation of the input. It is trained by mapping the input data to itself, with the compressed form in between. ...
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  • 166
1 vote

What is the best way to find the similarities between two text documents?

I would suggest to convert the documents into TF-IDF(use Gensim) vectors and then compare them using various similarity calculating techniques like cosine similarity. You should read this amazing ...
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1 vote

How do I plot a matrix of ratings?

I did it! ...
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1 vote
Accepted

When is content-based more appropriate than collaborative filtering?

Some of the cases content-based filtering is useful is: Cold-start problem: it happens when no previous information about user history is available to build collaborative filtering, so in this case, ...
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  • 94

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