Recommendation System Project Machine Learning

Machine learning based recommendation system construction is a famous concept that provides an importance of recommendation framework in various applications such as music streaming, online purchasing and movie suggestions. Below, we describe the procedural flow to develop a recommendation framework:

1. Problem Description:

Describe the type of recommendation framework that we intend to develop:

• Collaborative Filtering: Based on customer-product communications, our work suggests products.
• Content-Based Filtering: In this, by comparing content characteristics, we suggest products.
• Integrated Model: We integrate collaborative and content-based filtering approaches.
• Learning-to-Rank: Our project ranks the provided group of products for customers.

2. Data Collection:

• Initially, we begin with the datasets such as MovieLens dataset and also acquire datasets having customer-product communications (buying history, reviews, clicks, etc.).
• Gather product metadata (for instance: item description, film categories), if we are dealing with a content-based approach.

3. Preprocessing of Data:

• In the preprocessing steps, we manage the missing values.
• Our work encodes categorical information.
• If required, we normalize or standardize number-based data.

4. Exploratory Data Analysis (EDA):

• We interpret the distribution of reviews or customer communications.
• In customer activities, we detect patterns.
• For content-based filtering, our project examines the distribution of product metadata.

5. Feature Engineering:

• Our work intends to transform product metadata into feature vectors (for example: TF-IDF for text-related data) in a content-based filtering approach.
• We directly utilize customer and product IDs with techniques such as matrix factorization in collaborative filtering.

6. Model Chosen:

• Collaborative Filtering: In this approach, we employ deep learning frameworks like neural collaborative filtering and matrix factorization techniques (for instance: Singular Value Decomposition).
• Content-based Filtering: Our work utilizes decision trees, Cosine similarity and Neural networks assist us to work on product features.
• Integrated Model: We integrate the frameworks from the above two approaches.

7. Training the Model:

• Our project divides the data into training sets and validation or testing sets.
• By using training dataset, we train our chosen framework

8. Evaluation:

• Root Mean Square Error (RMSE): We consider this for frameworks which forecast ratings.
• Precision@k, Recall@k: For models which suggest a list of products, we utilize these metrics.
• Mean Average Precision, NDCG: Our project considers this specifically for ranking tasks.

9. Optimization:

• In this process, we adjust the hyperparameters.
• Our work considers the utilization of integrated techniques.

10. Deployment:

• With a mobile or web application, we combine our recommendation framework.
• Our research offers customers with suggestions in actual-time or batch mode.

11. Feedback Loop:

• We capture the customer reviews on suggestions.
• Our goal is to frequently reconstruct and retrain the framework through the utilization of these reviews.

Tools & Libraries:

• Data Handling & EDA: For this purpose, we use Seaborn, NumPy, pandas or Matplotlib.
• Collaborative Filtering: In this approach, our project considers TensorFlow, Surprise library or Keras.
• Content-Based Filtering: We utilize Scikit-learn for cosine similarity and TF-IDF.

Considerations:

• Cold Start Problem: It is difficult for us to manage the new customers or products without any communications. Therefore, at first, utilize integrated models or content-based suggestions.
• Diversity: Make sure our framework doesn’t suggest similar kinds of products frequently, because it leads to a filter bubble.
• Scalability: For huge-scale suggestion frameworks, we need distributed computing approaches such as Spark (with the use of ALS technique for collaborative filtering).

We conclude that the creation of a recommendation framework is a complicated one but it is a very beneficial attempt, providing its actual-world applications and effectively improving customer experience in several applications. Often we need to retrain our framework, integrate customer reviews and adapt to modify patterns and suggestions.  

Recommendation System Project Machine Learning   Thesis Ideas

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1. A Survey on Movie Recommendation System by using Machine Learning Algorithm

Keywords

Machine Learning, Content-based Filtering, RMSE, XGBoost, Recommendation System, Collaborative Filtering

            For effective recommendation of cinema to all the users based on their choices and searches, ML approach is developed based on latest method XGBoost to enhance the efficiency is the major aim of our study by utilizing collaborative filtering related movie system. Based on different genres among movies, information screening is employed to suggest films. To assess the genre based on the subscriber, filter is also applied and provides the appropriate movie.

2. Machine Learning based Ideal Job Role Fit and Career Recommendation System

Keywords

Job Recommendation, Graduate interests

            Our suggested model assists the newly graduated students to search for an appropriate job in an effective manner. For all the job searchers, this model suggests apt job based on their education qualifications. By employing ML approaches, the needs of the newly graduates are inspected. By the same way, autonomous recommendation model also inspect the student’s basic data, related fields, interests through their profiles.

3. Crop Recommendation System using Random Forest Algorithm in Machine Learning

Keywords

Agriculture, Crop Recommendation system, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machines

            Our research is about crop recommendation framework that is carried out by using ML methods. For the given zone, we suggested an appropriate crop based on various factors. Several ML methods such as KNN, Decision Tree, Random Forest, and SVM are examined. From the analysis, Random Forest is considered as a suitable method for crop recommendation. This framework will assists farmers and researchers in decision making based on crop suggestion.

4. Music Recommendation System Using Machine Learning

Keywords

Hybrid Recommendation System

            A music recommendation model is suggested in our study by employing ML approaches. This framework includes three phases; they are collaborative filtering, content based and approach based on half a breed. Based on the reviews of previous listeners, the suggested song list is organized utilizing K-means technique. Therefore, without wasting the time, this model will assist the users to select the songs of their own choice effectively.

5. Crop and Nutrient Recommendation System using Machine Learning for Precision Agriculture

Keywords

Crop suggestion, Nutrient suggestion

            Crop suggestion approach is recommended in our article that will help the farmers to decide wisely through the evaluation of soil’s enormous factors. This approach is carried out by employed ML methods. We utilized data related to crop to forecast whether the crop is suitable for the farmer’s land or not. This approach also suggested some information about the soil’s nutrient contents with regards to the crops that the farmers wanted to cultivate.

6. Machine Learning based Candidate Recommendation System using Bayesian Model

Keywords

Bayesian Technique, Artificial Intelligence, Job Seeker, Service Learning

            Our study utilizes ML methods to develop candidate suggestion model through Bayesian Model. An approach is also discussed that integrates Simple Bayesian Classifier with user- and item-based collaborative filtering to enhance the prediction precisely. Results show that, this integrated technique achieved greater efficiency than individual collaborative suggestion technique. This suggested model will assist both job searcher and recruiters.

7. An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Keyword

Data Analysis

            An intelligent model is suggested in our paper by communicating with customers’ needs and enormous data for providing suggestions. A new collaborative filtering suggestion model is recommended to develop a hotel feature matrix using polarity detection based on individual sentiment evaluation. Various factors are combined to recognize the activities of hotel customers. Related to the travel choice, this model would suggest hotels to the users.

8. Machine Learning-Based Personalized Recommendation System for E-Learners

Keywords

E-Learning, Filtering techniques, Automatic Speech Recognition (ASR), Optical character recognition (OCR)

            A recommendation system utilized in online learning platform is proposed in our research. To recommend appropriate videos that are related to the learner’s topics, an infrastructure is suggested for creating ML based Personalized Recommendation Systems. To forecast the request related websites, efficiency of ML methods are evaluated. As a result, Random forest algorithm provides effective outcomes.

9. Boosting of fruit choices using machine learning-based pomological recommendation system

Keywords

Precision agriculture, Cloud computing, Internet of Things, Agricultural productivity, Pomology

            Our paper recommended an innovative model by employing ML techniques that suggests suitable fruit to grow in the farmers land by considering the current climatic and soil state. Two new approaches such as Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) are employed to improve the framework’s performance. Finally, our model provides better performance in suggesting appropriate fruit based on the circumstances.

10. Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Keywords

Injection molding, Parameter recommendation, CAE simulation, Process window, Genetic algorithm

            For optimizing the injection molding procedure metrics, a recommendation framework is suggested in our paper. This framework includes various techniques like eXtreme Gradient Boosting (XGBoost), genetic algorithms and the use of process windows. To produce process window and simulation information, CAE simulations are focused. To forecast the best injection molding process metrics, SEGA method is employed.