Large refactor

scenarios/README.md

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+# Recommendation System Scenarios
+
+On this section there is listed a number of business scenarios that are common in Recommendation Systems.
+
+The list of scenarios are:
+
+* [Ads](ads)
+* [Entertainment](entertainment)
+* [Food and restaurants](food_and_restaurants)
+* [News and document]()
+* [Retail](retail)
+* [Travel](travel)
+
+
+
+

scenarios/ads/README.md

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+# Recommendation systems for Advertisement

scenarios/entertainment/README.md

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+# Recommendation systems for Entertainment

scenarios/food_and_restaurants/README.md

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+# Recommendation systems for Food and Restaurants

scenarios/news/README.md

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+# Recommendation systems for News

scenarios/retail/README.md

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+# Recommendation systems for Retail
+
+An increasing number of online companies are utilizing recommendation systems (RS) to increase user interaction and enrich shopping potential. Use cases of recommendation systems have been expanding rapidly across many aspects of eCommerce and online media over the last 4-5 years, and we expect this trend to continue.
+
+Companies across many different areas of enterprise are beginning to implement recommendation systems in an attempt to enhance their customer’s online purchasing experience, increase sales and retain customers. Business owners are recognizing potential in the fact that recommendation systems allow the collection of a huge amount of information relating to user’s behavior and their transactions within an enterprise. This information can then be systematically stored within user profiles to be used for future interactions.
+
+## Typical Business Scenarios in Recommendation Systems for Retail
+
+The most common scenarios companies use are:
+
+* Others you may like (also called similar items): The "Others you may like" recommendation predicts the next product that a user is most likely to engage with or purchase. The prediction is based on both the entire shopping or viewing history of the user and the candidate product's relevance to a current specified product.
+
+* Frequently bought together"(shopping cart expansion): The "Frequently bought together" recommendation predicts items frequently bought together for a specific product within the same shopping session. If a list of products is being viewed, then it predicts items frequently bought with that product list. This recommendation is useful when the user has indicated an intent to purchase a particular product (or list of products) already, and you are looking to recommend complements (as opposed to substitutes). This recommendation is commonly displayed on the "add to cart" page, or on the "shopping cart" or "registry" pages (for shopping cart expansion).
+
+* Recommended for you: The "Recommended for you" recommendation predicts the next product that a user is most likely to engage with or purchase, based on the shopping or viewing history of that user. This recommendation is typically used on the home page.
+
+
+## Data in Recommendation Systems
+
+### Data types
+
+In RS for retail there are typically the following types of data
+
+* Explicit interactions: When a user explicitly rate an item, typically between 1-5, the user is giving a value on the likeliness of the item. In retail, this kind of data is not very common.
+
+* Implicit interactions: Implicit interactions are views or clicks that show a certain interest of the user about a specific items. These kind of data is more common but it doesn't define the intention of the user as clearly as the explicit data.
+
+* User features: These include all information that define the user, some examples can be name, address, email, demographics, etc. 
+
+* Item features: These include information about the item, some examples can be SKU, description, brand, price, etc.
+
+* Knowledge graph data: ...
+
+### Considerations about data size
+
+The size of the data is important when designing the system...
+
+### Cold start scenarios
+
+Personalized recommender systems take advantage of users past history to make predictions. The cold start problem concerns the personalized recommendations for users with no or few past history (new users). Providing recommendations to users with small past history becomes a difficult problem for CF models because their learning and predictive ability is limited. Multiple research have been conducted in this direction using hybrid models. These models use auxiliary information (multimodal information, side information, etc.) to overcome the cold start problem.
+
+### Long tail products
+
+Typically, the shape of items interacted in retail follow a long tail distribution [1,2]. 
+
+## Measuring Recommendation performance
+
+### Machine learning metrics (offline metrics)
+
+Offline metrics in RS are based on rating, ranking, classification or diversity. For learning more about offline metrics, see the [definitions available in Recommenders repository](../../examples/03_evaluate)
+
+### Business success metrics (online metrics)
+
+Below are some of the various potential benefits of recommendation systems in business, and the metrics that tipically are used:
+
+* Click-through rate (CTR): Optimizing for CTR emphasizes engagement; you should optimize for CTR when you want to maximize the likelihood that the user interacts with the recommendation.
+
+* Revenue per order: The revenue per order optimization objective is the default optimization objective for the "Frequently bought together" recommendation model type. This optimization objective cannot be specified for any other recommendation model type.
+
+* Conversion rate: Optimizing for conversion rate maximizes the likelihood that the user purchases the recommended item; if you want to increase the number of purchases per session, optimize for conversion rate.
+
+### Relationship between online and offline metrics in retail
+
+There is some literature about the relationship between offline and online metrics...
+
+
+### A/B testing
+
+### Advanced A/B testing: online learning with VW
+
+...
+
+## Examples of end 2 end recommendation scenarios with Microsoft Recommenders
+
+From a technical perspective, RS can be grouped in these categories [1]:
+
+* Collaborative filtering: This type of recommendation system makes predictions of what might interest a person based on the taste of many other users. It assumes that if person X likes Snickers, and person Y likes Snickers and Milky Way, then person X might like Milky Way as well. See the [list of examples in Recommenders repository](../../examples/02_model_collaborative_filtering).
+
+* Content-based filtering: This type of recommendation system focuses on the products themselves and recommends other products that have similar attributes. Content-based filtering relies on the characteristics of the products themselves, so it doesn’t rely on other users to interact with the products before making a recommendation. See the [list of examples in Recommenders repository](../../examples/02_model_content_based_filtering).
+
+* Hybrid filtering: This type of recommendation system can implement a combination fo any two of the above systems. See the [list of examples in Recommenders repository](../../examples/02_model_hybrid).
+
+* Knowledge-base: ...
+
+In the repository we have the following examples that can be used in retail
+
+| Scenario | Description | Algorithm | Implementation |
+|----------|-------------|-----------|----------------|
+| Collaborative Filtering with explicit interactions in Spark environment |  Matrix factorization algorithm for explicit feedback in large datasets, optimized by Spark MLLib for scalability and distributed computing capability | Alternating Least Squares (ALS) | [pyspark notebook using Movielens dataset](https://github.com/microsoft/recommenders/blob/staging/notebooks/00_quick_start/als_movielens.ipynb) |
+| Content-Based Filtering for content recommendation in Spark environment | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems | LightGBM/MMLSpark | [spark notebook using Criteo dataset](https://github.com/microsoft/recommenders/blob/staging/notebooks/02_model/mmlspark_lightgbm_criteo.ipynb) |
+
+
+
+## References and resources
+
+[1] Aggarwal, Charu C. Recommender systems. Vol. 1. Cham: Springer International Publishing, 2016.
+[2]. Park, Yoon-Joo, and Alexander Tuzhilin. "The long tail of recommender systems and how to leverage it." In Proceedings of the 2008 ACM conference on Recommender systems, pp. 11-18. 2008. [Link to paper](http://people.stern.nyu.edu/atuzhili/pdf/Park-Tuzhilin-RecSys08-final.pdf).
+[3]. Armstrong, Robert. "The long tail: Why the future of business is selling less of more." Canadian Journal of Communication 33, no. 1 (2008). [Link to paper](https://www.cjc-online.ca/index.php/journal/article/view/1946/3141).
+
+sources: [1](https://emerj.com/ai-sector-overviews/use-cases-recommendation-systems/), [2](https://cloud.google.com/recommendations-ai/docs/placements), [3](https://www.researchgate.net/post/Can_anyone_explain_what_is_cold_start_problem_in_recommender_system)

scenarios/travel/README.md

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+# Recommendation systems for Travel