딥 FM: 고급 추천 시스템 아키텍처 설명

### 딥 FM의 이해: 추천 시스템에 대한 고급 접근 방식
* 📚 오늘은 인수분해 머신의 고급 버전인 **딥 FM과 이를 추천 시스템에 적용하는 방법**에 대해 알아보겠습니다.
* 🔍 이 토론에 앞서 **협업 필터링, 메트릭 인수분해, NCF, 인수분해 머신**과 같은 개념을 다루어 기초 지식을 제공했습니다.
* 📈 인수 분해 기계는 효과적인 추천을 위해 **중요한 사용자 및 항목 기능**과 함께 사용자-항목 상호 작용을 통합하여 **선형 회귀를 확장**합니다.
* 🤖 Deep FM은 **인수분해 머신의 신경망 변형**으로 작동하여 데이터의 비선형 관계를 포착하여 추천 프로세스를 향상시킵니다.
* 🔗 딥 FM의 아키텍처는 **입력 피처를 원핫 인코딩으로 변환**한 다음 각 피처에 대해 고정 크기의 임베딩을 생성하는 임베딩 레이어가 있는 NCF와 유사합니다.
* ⚙ 그런 다음 이러한 임베딩은 **인수분해 기계 부분과 심층 신경망(DNN) 부분의 두 가지 주요 구성 요소**를 통해 처리되어 최종 출력에 함께 기여합니다.
* 📊 두 구성 요소의 출력을 연결하고 시그모이드 활성화 함수를 적용하여 최종 권장 사항을 생성하는 것은 **인수분해 머신과 딥러닝 기술 간의 시너지**를 보여줍니다.
* 💡 전반적으로 딥 FM은 **임베딩 레이어와 신경망의 강점을 통합**하여 추천 시스템의 성능을 크게 향상시킵니다.

Deep FM은 복잡한 관계를 파악하기 위해 인수분해 머신과 심층 신경망을 결합하여 추천 시스템을 향상시킵니다.

uh, so hi everyone. Today we'll be discussing what deep FM is and how they can be used for recommendation systems. So let's get started. So prior to this, I have already explained what collaborative filtering is, what metric factorization, NCF, and factorization machine are. So today we will be talking about deep FM. So before moving on to deep FM, we must know that deep FMs are considered as an advanced version of factorization machines. So this is something that I've already explained in my previous videos. So first of all, we'll try to recapsulate what factorization machines are. So factorization machines are nothing but an extension of linear regression, where apart from capturing the user-item interaction, we're also using the user and the item features as well while making the recommendation system. So deep FM can be considered as a neural network version of factorization machine. So in the case of factorization machine, we are trying to capture higher-order relationships. By using deep FM, we would be trying to capture non-linear relationships as well. So to understand

what more about factorization machine how they were what is the equation you can follow up in a previous video so we're talking about the architecture of deep FM so deep FM like saying is pretty much similar to ncf so ncf is for ncif can be taken as an extension of collaborative filtering and Matrix factorization similarly a deep FM is an extension of factorization machine so we'll start off with all the features that we get assume that we have features field one field I field J till field M now this will include the user item interaction alongside user and item features also the next step is to convert these features into one or encode so if we have categorical features in this particular data set they would be converted into space into one hot encodes if the continuous switches will leave them as well as them as it is the next layer is an embedding layer so these one word encoded features will be then fed to dense embedding layer where we will be generating embeddings for each of the one hot encoded feature along the

current switches for each feature we will be generating an embedding of other fixed size. Now, once we have generated embeddings for each of the features, we do divide this architecture into two parts: one is the factorization machine part, and the other is the DNN part. So, let's first of all start with the input and output. So, as we convert that, deep FM is an extension of a factorization machine. The input is quite similar to what goes into the factorization machine, that is, one of the encoded features. The next layer is the embedding layer, as I have already told you. Now, once the embedding layer is generated, the embeddings are then passed onto two parts: one is the factorization machine, that is, the left side of the diagram, as told here, where we are following this particular equation: y equals to W 0 plus summation w i into x i, the summation summation uh these VI dot VJ. VI represents the latent vector x i into x j. So, to understand this whole equation, you need to go to my previous video and understand what this equation is all about.

about because I have already explained a factorization machine. Here, I have given you a level key. What is y-wise label? W is bias-wise weights. x_i is the features for one art encoder feature set. nww.red within latent vectors. A DOT product is basically the meaning that we are generating now. This is the left-hand side. Moving on to the right-hand side is a DNN. So, this is nothing but very similar to the ncf multi-layer perceptron that we developed. So, what happens here is that we're feeding all these embeddings into a general neural network, which comprises multiple hidden layers, and the final layer is a sigma. The final output goes through a sigma of activation function. So, basically talking about the final output, the output from the factorization machine, that is the left part, and the DNN, that is the right part, are then combined together, concatenated, and then a sigmoid activation function is applied. Deep FM can be taken as embedding layer and sigmoid over factorization machine plus DNN. That's it. It is nothing complicated and very, very

similar to NCO, where NCF is an extension for a metric factorization.

DeepFM for recommendation systems explained

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Deep FM은 인수분해 머신의 고급 버전으로, 사용자-아이템 상호작용에서 고차 관계와 비선형 관계를 모두 포착하는 것을 목표로 합니다.

Deep FM의 아키텍처는 인수분해 머신을 확장하는 데 중점을 둔다는 점에서 NCF와 유사합니다.

이 프로세스에는 피처 인코딩, 임베딩 생성, 추천을 위한 사용 등이 포함됩니다.

딥 FM: 인수분해 머신과 DNN의 결합

Deep FM은 피처에 대한 고정 크기의 임베딩을 생성하고 아키텍처를 인수분해 머신과 DNN의 두 부분으로 나눕니다.

인수분해 머신은 특정 방정식을 사용하여 임베딩에서 출력을 생성하고, DNN은 임베딩을 여러 개의 숨겨진 레이어가 있는 신경망에 공급합니다.

최종 출력은 인수분해 머신과 DNN 출력의 조합에 시그모이드 활성화 함수를 더한 것입니다.

NCF: 미터법 인수분해를 위한 확장 기능

NCF는 메트릭 인수분해를 위해 특별히 설계된 NCO 프레임워크의 확장입니다.

추가 메트릭을 통합하여 인수분해 방법의 성능을 향상시키는 것을 목표로 합니다.

이 접근 방식은 데이터 분석 및 머신 러닝의 다양한 애플리케이션을 위한 보다 포괄적인 솔루션을 제공합니다.