Area Of Work:
Recommendation Engine
,
Machine Learning
Artificial Intelligence - Recommendation Engine
Go beyond the one-size-fits-all solution and deliver the right content, product or service at the right time to your users with Oodles’ Recommendation Engine Development Servcies. Our AI-powered systems, driven by sophisticated algorithms and real-time processing, continuously learn, adapt, and seamlessly integrate with your platform; transforming your user data into powerful, personalized suggestions that boost engagement and fuel better conversions for your business.
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Understanding How Recommendation Engines Work
Understanding How Recommendation Engines WorkRecommendation engines are essential components of many modern applications and websites, playing a crucial role in delivering personalised experiences. They help users discover content, products, or services based on their preferences and behaviours. In this blog post, we will explore how recommendation engines work, the different types, and some common algorithms used in the process.What is a Recommendation Engine?A recommendation engine is a system that suggests items to users based on various criteria. This could include user behaviour, preferences, or demographic data. They are widely used in e-commerce, streaming services, social media, and more to enhance user engagement and satisfaction.How Recommendation Engines WorkRecommendation engines typically follow these steps:Data Collection: The first step involves gathering data. This can include user behaviour data (like clicks, views, purchases), demographic information, and even feedback (ratings or reviews).Data Processing: The collected data is then processed to extract meaningful features. This often involves cleaning the data, handling missing values, and transforming raw data into a usable format.Model Selection: The engine chooses a model or algorithm to generate recommendations. The choice of model depends on the type of recommendation approach used.Recommendation Generation: Based on the selected model, the engine generates a list of recommended items for each user.Feedback Loop: Recommendations are continuously refined based on user interactions with the suggested items, creating a feedback loop that helps improve the model over time.Types of Recommendation SystemsRecommendation systems can generally be categorised into three main types:1. Collaborative FilteringCollaborative filtering relies on the behavior and preferences of users. It can be further divided into:User-based Collaborative Filtering: This approach recommends items by finding similar users. If User A and User B have a high overlap in items they like, User A might receive recommendations based on what User B enjoys.Item-based Collaborative Filtering: This method looks at item similarity rather than user similarity. If a user likes a particular item, the system recommends other items that are frequently liked together.2. Content-based FilteringContent-based filtering recommends items based on the features of the items themselves. For instance, in a movie recommendation system, if a user enjoys action films, the engine might recommend other movies that are labelled as action. This approach requires detailed information about the items, such as genre, director, or keywords.3. Hybrid SystemsHybrid systems combine both collaborative and content-based filtering methods. By leveraging the strengths of both approaches, hybrid systems can provide more accurate and diverse recommendations. For example, Netflix uses a hybrid model to suggest movies and shows to its users.Common AlgorithmsSeveral algorithms are commonly used in recommendation engines:Matrix Factorization: This technique decomposes the user-item interaction matrix into lower-dimensional matrices, revealing latent factors that explain user preferences.K-Nearest Neighbors (KNN): This algorithm identifies similar users or items based on distance metrics and provides recommendations accordingly.Deep Learning: Neural networks, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can model complex relationships in data for more nuanced recommendations.Challenges in Recommendation SystemsWhile recommendation engines are powerful, they face several challenges:Cold Start Problem: New users or items without historical data can hinder the effectiveness of collaborative filtering.Scalability: As the user base and item catalog grow, the computation required for generating recommendations can increase significantly.Diversity vs. Accuracy: Striking a balance between providing accurate recommendations and offering diverse options is essential for user satisfaction.ConclusionRecommendation engines have transformed how we interact with technology, making experiences more personalized and engaging. Understanding the underlying mechanisms can help businesses improve their offerings and better serve their users. As data and algorithms continue to evolve, the future of recommendation systems promises even more sophisticated and effective methods of understanding user preferences. Whether you're an entrepreneur, developer, or simply a curious mind, exploring this field can reveal a wealth of insights and opportunities.
Harshit Laxkar
08 Oct 2024
Smart Chatbot with AWS Bedrock, LLAMA-Index, and PostgreSQL
Smart Chatbot with AWS Bedrock, LLAMA-Index, and PostgreSQLEver wished you could have a chatbot that does more than just answer basic questions? How about one that understands complex queries, speaks multiple languages, schedules your meetings, and even generates reports at the start and end of your day? Well, that's exactly what we built using AWS Bedrock, LLAMA-Index, and PostgreSQL.In this blog, I'll walk you through how we created a chatbot that feels like an extra team member rather than just a machine. From answering queries with real-time data to handling important tasks like meeting schedules and report generation, this bot does it all—and in a friendly, human-like way.The Tech Stack Behind the MagicBefore we dive into the chatbot's features, here's a quick rundown of the tools we used to build it:AI Framework: LLAMA-IndexAI LLM Service: AWS BedrockLanguage Model (LLM): MISTRADatabase: PostgreSQLProgramming Language: PythonEach of these technologies plays a critical role in making the bot intelligent, responsive, and functional.How the Chatbot Works: Features That Make It Special1. Conversational Intelligence Meets DatabasesThe heart of this chatbot is LLAMA-Index, an AI framework that makes it easy for the bot to interact with databases like PostgreSQL. When you type in a question, the bot isn't just giving a generic response; it's actually understanding the structure of a relational SQL database and fetching specific, meaningful answers.This is made possible through NLSQL (Natural Language SQL), which allows the chatbot to interpret natural language questions and map them to database queries. So, whether you're asking for a sales report or trying to pull up employee records, the chatbot can grab the right data from PostgreSQL and deliver it back to you in a friendly, conversational tone.2. Chatting in Multiple LanguagesLet's say you speak Spanish or French—no problem! The chatbot can communicate in multiple languages, responding in the language you used to ask the question. This feature makes it not only smart but also versatile, catering to users from different linguistic backgrounds.3. Scheduling Meetings EffortlesslyHere's where the chatbot becomes a personal assistant. You can provide meeting details—like date, time, participants, and agenda—and the bot will automatically store this information in PostgreSQL. By leveraging LLAMA-Index's Function Tool, the chatbot understands your scheduling requests and handles the whole process seamlessly. No more bouncing between apps or missing calendar invites!4. Daily Reports with a Friendly ToneWe also equipped the chatbot with the ability to generate reports at the start and end of your day. These reports can include anything from daily tasks and deadlines to summaries of completed work. The bot can:Generate a Start-of-Day Report: What's on the agenda? What meetings do you have? What are today's priorities?Create an End-of-Day Report: What tasks were completed? What's still pending? Any key achievements?Thanks to Function Tools in LLAMA-Index, the bot doesn't just retrieve data—it actively interprets your input, decides what kind of report you need, and delivers it in a friendly, human-readable format.The Workflow: How It All Comes TogetherHere's a quick breakdown of what happens behind the scenes when you interact with the chatbot:User Input: You type a query, like "Show me today's sales report" or "Schedule a meeting for tomorrow at 10 AM."LLM Interpretation: The language model (MISTRA from AWS Bedrock) processes your input, understanding the intent behind your words.NLSQL Conversion: The query gets converted into SQL, fetching data from PostgreSQL.Response Generation: The bot provides a response that's both accurate and conversational.Additional Tasks: For tasks like scheduling meetings or generating reports, the chatbot calls specific functions through LLAMA-Index's Function Tool, making the experience feel almost like you're talking to a human assistant.LLAMA-Index's Function Tool: The Secret SauceWhat really sets this bot apart is the Function Tool within LLAMA-Index. It allows the bot to:Understand the context of the user's request (whether it's related to the database, scheduling, or generating reports).Take action by calling relevant functions (like SQL queries for data retrieval or creating calendar entries for meetings).This dynamic feature makes the chatbot more than just a tool for information—it becomes a problem-solver and task manager, tailored to your needs.Wrapping It UpWe've come a long way from basic chatbots that only provide canned responses. This AI-powered assistant, built using AWS Bedrock, LLAMA-Index, and PostgreSQL, is more than just a chatbot—it's an intelligent, multilingual helper that understands complex queries, interacts with databases, and even handles your daily tasks.Whether you need help scheduling meetings, generating reports, or retrieving specific data from your database, this bot has got you covered. It's like having a team member who's always available, always accurate, and speaks your language—literally!Why You Should Consider Building a Chatbot Like ThisWith the right combination of tools and AI frameworks, you can create a chatbot that not only understands your needs but also takes action to make your life easier. If you're interested in automating tasks or enhancing your business workflows, this tech stack is a great place to start.
Area Of Work:
Generative AI
,
Chat bot
more
Sanjeev Kumar
17 Sep 2024
Factors Affecting a Recommendation System
Factors Affecting a Recommendation SystemRecommendation systems play a strong role in shaping user experiences in the modern world. Whether you are on Netflix, trying to find products on Amazon, or simply scrolling down your favourite social media feed, these systems help you find new and interesting content and products. So, what really matters about how these systems work?In this post, I'll break up key factors that influence a recommendation system and walk through some simple code snippets that illustrate these ideas.1. User DataAny recommendation system is basically built on top of user data. The more detailed and the more accurate data you have about preferences of your users, the better recommendations your system can make. Such data can be explicit-ratings and likes, for example-or implicit-browsing history and clicks, for instance.For example, a minimalistic user profile will look like this:user = {'id': 1,'name': 'John Doe','preferences': {'genres': ['action', 'comedy'],'liked_items': [101, 202, 303]},'browsing_history': [404, 505, 606]}2. Item DataThe item data is actually the thing you're recommending-that is, its contents or product information. This includes metadata about genres, tags, descriptions, and so on. The better your system is at mirroring the preferences of the user regarding items, the richer the data involved will be.items = [{'id': 101, 'title': 'Action Movie 1', 'genres': ['action', 'thriller']},{'id': 202, 'title': 'Comedy Show', 'genres': ['comedy']},{'id': 303, 'title': 'Documentary', 'genres': ['history', 'educational']}]3. Collaborative FilteringCollaborative filtering is a technique which utilises the preferences of similar users to make recommendations. In case when two users liked the same kind of items in the past, then it is likely that they will like the same kinds of items again.For example, suppose that User A and User B have liked "Action Movie 1" and User A also liked "Comedy Show," then "Comedy Show" would be recommended to User B.def get_similar_users(user, all_users):return [other_user for other_user in all_usersif any(genre in user['preferences']['genres']for genre in other_user['preferences']['genres'])]all_users = [{'id': 2, 'preferences': {'genres': ['action', 'drama'], 'liked_items': [101, 404]}},{'id': 3, 'preferences': {'genres': ['comedy', 'thriller'], 'liked_items': [202, 505]}}]similar_users = get_similar_users(user, all_users)4. Content-Based FilteringThis type of filtering filters items on the basis of characteristics of the items themselves, meaning an item recommended would be similar to what a user interacts with. For example, if a user likes action movies, the system will suggest more action movies based on what other users have liked.def recommend_based_on_content(user, items):return [item for item in itemsif any(genre in user['preferences']['genres']for genre in item['genres'])]recommended_items = recommend_based_on_content(user, items)print(recommended_items) # [{'id': 101, 'title': 'Action Movie 1', 'genres': ['action', 'thriller']}]5. Cold Start ProblemThe "cold start" problem is one of the issues with recommendation systems. This is when a new user or a new item enters the system, and there is not much to go on for recommendations. We could use popularity trends in general, demographic data, or ask users to set up their first preferences.def recommend_popular_items(popular_items):return popular_items[:5] # Recommend top 5 popular itemspopular_items = [{'id': 505, 'title': 'Blockbuster Movie', 'genres': ['action', 'drama']},{'id': 606, 'title': 'Hit Comedy', 'genres': ['comedy']}]print(recommend_popular_items(popular_items)) # Top 5 recommendations6. Personalization vs. DiversityThe perfect recommendation system should, therefore, attain harmony between personalizing to the individual's tastes and ensuring diversity. If such a system really displays the most similar items that the user has seen, this would considerably limit their discovery. Deviation from diversity ensures that users are not exposed to new genres or contents in which they could not anticipate their fun.import randomdef diversify_recommendations(recommendations, all_items):diversified = list(recommendations)random_item = random.choice(all_items)if random_item not in recommendations:diversified.append(random_item)return diversifiedDiversity helps prevent the recommendation system from becoming stale by adding new or unexpected items into the mix.7. Feedback LoopsGood recommendations also rely on the feedback the system receives. Positive feedback, such as ratings or clicks will hone the system so that future recommendations will be even closer to your actual preferences. Negative feedback-like skipping or disliking-will do the same, except their effect is opposite in direction.def update_user_preferences(user, new_like):user['preferences']['liked_items'].append(new_like)update_user_preferences(user, 404)ConclusionRecommendation systems are complex. But their reasons are obvious. Generally, factors of user data, item data, collaborative filtering, content-based filtering, and the cold start problem determine the change in the system. Understanding these factors can help us create more personalized and better experiences for the users.
Area Of Work:
Recommendation Engine
,
Machine Learning
Harshit Laxkar
08 Oct 2024
Building a Recommendation Model with KNN
Building a Recommendation Model Using K-Nearest Neighbors (KNN)Recommendation systems are everywhere these days. From e-commerce sites to recipe apps, they are all around. One of the simplest and most intuitive way to build a recommendation engine is the K-Nearest Neighbors (KNN) algorithm. Here we will walk through how to build a recommendation engine using a pipeline for preprocessing and scaling with code snippets for each step.K-Nearest Neighbors (KNN)KNN is a non-parametric, instance-based learning algorithm. It works by finding the k nearest data points (neighbors) to a given input and then providing recommendations based on the similarity between these points. In our recommendation engine, we will be using cosine similarity as the metric to determine how similar data points (recipes or food items) are to each other.The steps to build the recommendation model are:Extract the relevant columns for nutrition content.Scale the data so all features are comparable.Use KNN to find the nearest neighbors based on cosine distance.Build a pipeline.Filter the data based on user preferences (e.g. include or exclude specific ingredients).Make recommendations and calculate accuracy.Now, let's get into the code.Step 1: Extract Nutrition ColumnsFirst we need to extract the relevant nutrition columns, such as calories, fat, protein etc. These features are used to measure the similarity between different food items.def extract_nutrition_columns(dataframe):
columns = ['Calories', 'FatContent', 'SaturatedFatContent', 'CholesterolContent',
'SodiumContent', 'CarbohydrateContent', 'FiberContent', 'SugarContent', 'ProteinContent']
return dataframe[columns]Step 2: Scale the DataWe scale the data so no single feature dominates (e.g. calories vs fiber) using StandardScaler.Here we use StandardScaler to standardize the features by removing the mean and scaling to unit variance. This makes the KNN algorithm work better.from sklearn.preprocessing import StandardScaler
def scaling(dataframe):
scaler = StandardScaler()
prep_data = scaler.fit_transform(dataframe.to_numpy())
return prep_data, scalerStep 3: KNN PredictorNext we use the KNN algorithm to create a model that will find the nearest neighbors based on cosine similarity.We use cosine metric because it measures the cosine of the angle between two vectors, perfect for measuring similarity between food items based on nutrition values.from sklearn.neighbors import NearestNeighbors
def nn_predictor(prep_data):
neigh = NearestNeighbors(metric='cosine', algorithm='brute')
neigh.fit(prep_data)
return neighStep 4: PipelineWe then build a pipeline to chain the scaling and neighbor prediction steps together so we can apply the model to new inputs.The pipeline makes the code cleaner and more modular. It ensures the data is scaled before it goes into the KNN model.from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
def build_pipeline(neigh, scaler, params):
transformer = FunctionTransformer(neigh.kneighbors, kw_args=params)
pipeline = Pipeline([('std_scaler', scaler), ('NN', transformer)])
return pipelineStep 5: Filter Data by TagsSometimes users want to include or exclude specific ingredients from the recommendations. We implement a function to filter data by the given tags.def extract_ingredient_filtered_data(dataframe, include_tags=None, exclude_tags=None):
extracted_data = dataframe.copy()
def filter_row(tags_string):
if tags_string:
tags = [tag.strip().lower() for tag in tags_string.split(',')]
if include_tags:
for tag in include_tags:
if tag.lower() not in tags:
return False
if exclude_tags:
for tag in exclude_tags:
if tag.lower() in tags:
return False
return True
extracted_data = extracted_data[extracted_data['Tags'].apply(filter_row)]
return extracted_dataStep 6. Make RecommendationsUse the pipeline and KNN model to make recommendations based on the user's input.import numpy as np
def apply_pipeline(pipeline, _input, extracted_data):
_input = np.array(_input).reshape(1, -1)
data = pipeline.transform(_input)
return extracted_data.iloc[data[1][0]], data[0][0]Step 7: Calculating AccuracyWe calculate the "accuracy" based on how close the recommended items are to the input data.This simple function converts the cosine distances into percentage-based accuracy scores.def get_accuracy(distances):
accuracy = [100 - (i * 100) for i in distances]
return accuracy
Step 8. Combine all the stepsThe recommend function combines all the steps to make recommendations. It first filters the data based on tags, extracts the relevant nutrition columns, scales the data, and applies the KNN model to provide the nearest neighbors.def recommend(dataframe, _input, include_tags=[], exclude_tags=[], n_neighbors=5):
extracted_data = dataframe.copy()
params = {'n_neighbors': n_neighbors, 'return_distance': True}
if include_tags or exclude_tags:
extracted_data = extract_ingredient_filtered_data(dataframe, include_tags, exclude_tags)
params['n_neighbors'] = min(params['n_neighbors'], extracted_data.shape[0])
if params['n_neighbors'] == 0:
return None, None
extracted_cols = extract_nutrition_columns(dataframe)
prep_data, scaler = scaling(extracted_cols)
neigh = nn_predictor(prep_data)
pipeline = build_pipeline(neigh, scaler, params)
data, distances = apply_pipeline(pipeline, _input, extracted_data)
return data, get_accuracy(distances)ConclusionUsing KNN for recommendation systems is a straightforward yet powerful approach. By combining data preprocessing, filtering, and the KNN algorithm, we've built a model that can make personalized recommendations based on nutritional content. The modular nature of this implementation makes it adaptable to various applications, from food recommendations to product suggestions.You can easily extend this model by incorporating more features, experimenting with different distance metrics, or enhancing the filtering mechanism based on user preferences.
Area Of Work:
Recommendation Engine
,
Machine Learning
Harshit Laxkar
10 Oct 2024
What are recommendation systems
E-commerce and marketing companies leverage data capabilities and improve sales through promotional systems on their websites. The conditions for using these systems have been steadily increasing over the years, and it is a great time to probe deeper into this excellent machine learning process.
Recommendation programs aim to predict users' interests and recommend product items that may interest them. They are one of the most powerful machine learning programs developed by online retailers to drive sales.
The data required in the recommendation system is based on explicit user ratings after watching a video or a song, from queries and purchase queries of a search engine or other information about users or the product itself.
Many sites like YouTube, Netflix, Spotify, etc., use data to promote playlists called Daily Mixes or make video recommendations.
Types of Recommendations:
Of the many categories of Recommendation Systems, the two most widely used branches today are:
Collaborative-Based Recommendation Programs:
These programs work by collecting user comments in the form of ratings. Matching metrics are calculated for group users with the same object rating. In addition, recommendations are made to the user based on the opinions of other users.
Content-Based Recommendation Programs:
In this category, items are recommended based on their content information rather than other users' opinions, such as the Based Filtering Program.
We can further divide Integrated Recommendation Programs into two categories:
Model-based collaborative filtering: In this type of collaborative filtering method, user ratings are collected and used to predict the expected value of the user prediction when given user ratings for other items. Different machine learning algorithms such as the Bayesian, Clustering, and Rule-based approach networks are used to build this type of Collaborative Filtering-based system.
Memory-based collaborative filtering: (Also known as Neighborhood) is the type of collaborative filtering method in which we use the entire user object database for prediction. Statistical techniques are used to find active user neighbours and to combine their preferences to generate predictions.
Applications of Recommendations System:
A predictive analysis and recommendation program can benefit almost any business. Two key factors that determine how business benefits from a recommendation process are:
Data Scope: A business that only works for a handful of customers who behave differently will not get much benefit from an automated recommendation system. People are still much better off than machines in the learning environment. In such a case, your employees will use their understanding and customer quality to make accurate recommendations.
Data depth: Having one data point per customer does not help in recommendation programs—detailed information about online customer services and, where possible offline purchases, may guide through accurate recommendations. Using this framework, we can identify industries that will benefit from the promotional programs.
E-Commerce: It is the industry in which prediction systems were first used. With millions of customers and data on their online platform, e-commerce companies are well prepared to produce accurate recommendations.
Sales: Terrified targets were feared back in the 2000s when Target systems predicted pregnancy even before mothers detected their pregnancies. Purchase data is the most critical data as it is the most relevant data point in the customer's intention. Vendors with a wealth of information buyers are at the forefront of companies making accurate recommendations.
The media: Similar to e-commerce, media businesses were one of the first to enter the recommendations. It's hard to see a news site without a recommendation plan.
Banking: Public banking and SMEs are significant in the recommendations. Knowing the detailed financial status of the customer and their previous preferences, which are consistent with the data of thousands of similar users, has excellent potential.
Telecommunications: It shares the same flexibility with banking. Telcos have access to millions of customer subscriptions across all communications. Their product range is also limited compared to other industries, making recommendations on telecom an easy task.
Resources: Power is the same as telecom, but resources have a smaller range of products, making recommendations easier.
The conclusion
Since Amazon published its collaborative filter paper, the platform for promotional programs has grown exponentially. In contrast, this offers many options to suit different use cases and makes system selection very difficult. Some of the many things to consider include:
What are the business objectives and metrics used to evaluate system performance?
In addition to standard metrics such as accuracy and inclusion, other factors to consider include diversity, homosexuality, and youth (as discussed in the preceding paragraphs).
How can you handle the first cold problem of new users or new things? What is the latency you want to predict (and maybe how much training time is acceptable)? This mainly depends upon the model complexity, Variation, and what kind of hardware (type of model with AWS terms) are needed to support training and provide effective models? Also, this depends on the modeling of the model and will play a significant factor in the economic solution.
How is the model translated?
This can be a great need for business stakeholders.
Concerning implementation, many decisions could be made.
Area Of Work:
Recommendation Engine
Vikas Verma
30 Apr 2021
