User-User Collaborative Filtering For Jokes Recommendation

Have you ever had ants in your home? If you’ve had, you might know that ants first spread out individually looking for food. But as soon as one of them finds the food, it makes its way back to the nest, leaving behind the scented trail that other ants soon follow. And then you have a stream of ants heading back and forth the food source and the nest. Those ants are exhibiting social navigation, a type of recommendation system where each of the ants goes out and explores a different part of the space, literally space, and when they find something that they think the community would like, they let everyone know about it.

Recommendation Systems are a big part of today’s world. Customers may see a lot of available options and not know what to buy. They may be unaware of a product that serves their purpose, or maybe a movie or a song or joke they will eventually like but they haven’t heard about it yet. This is why recommendation systems are used. They make specific recommendations to customers to overcome the above-mentioned problems. They may recommend items based on its content (content-based recommendation), based on user’s session activities (sequential or session-based recommendation), based on items that similar users like (user-user collaborative filtering), or based on the similarities with items that customer has liked previously (item-item collaborative filtering), or maybe a hybrid model of two or more of the above-mentioned systems.

In this article, we will focus on similar users based recommendation system, otherwise also known as user-user collaborative filtering, and apply it to give jokes recommendations.

#recommendation-engine #data-science #user-based-cf #python #collaborative-filtering

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User-User Collaborative Filtering For Jokes Recommendation

User-User Collaborative Filtering For Jokes Recommendation

Have you ever had ants in your home? If you’ve had, you might know that ants first spread out individually looking for food. But as soon as one of them finds the food, it makes its way back to the nest, leaving behind the scented trail that other ants soon follow. And then you have a stream of ants heading back and forth the food source and the nest. Those ants are exhibiting social navigation, a type of recommendation system where each of the ants goes out and explores a different part of the space, literally space, and when they find something that they think the community would like, they let everyone know about it.

Recommendation Systems are a big part of today’s world. Customers may see a lot of available options and not know what to buy. They may be unaware of a product that serves their purpose, or maybe a movie or a song or joke they will eventually like but they haven’t heard about it yet. This is why recommendation systems are used. They make specific recommendations to customers to overcome the above-mentioned problems. They may recommend items based on its content (content-based recommendation), based on user’s session activities (sequential or session-based recommendation), based on items that similar users like (user-user collaborative filtering), or based on the similarities with items that customer has liked previously (item-item collaborative filtering), or maybe a hybrid model of two or more of the above-mentioned systems.

In this article, we will focus on similar users based recommendation system, otherwise also known as user-user collaborative filtering, and apply it to give jokes recommendations.

#recommendation-engine #data-science #user-based-cf #python #collaborative-filtering

Ray  Patel

Ray Patel

1623145380

Item-Based Collaborative Filtering in Python

The practice of making the item-based collaborative filtering in python.

Item-based collaborative filtering  is the recommendation system to use the similarity between items using the ratings by users. In this article, I explain its basic concept and practice how to make the item-based collaborative filtering using Python.

Basic Concept

Making a Movie Recommender

#item-based-cf #python #collaborative-filtering #movie-recommendation #item-based collaborative filtering #recommender

How To Create User-Generated Content? [A Simple Guide To Grow Your Brand]

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In this digital world, online businesses aspire to catch the attention of users in a modern and smarter way. To achieve it, they need to traverse through new approaches. Here comes to spotlight is the user-generated content or UGC.

What is user-generated content?
“ It is the content by users for users.”

Generally, the UGC is the unbiased content created and published by the brand users, social media followers, fans, and influencers that highlight their experiences with the products or services. User-generated content has superseded other marketing trends and fallen into the advertising feeds of brands. Today, more than 86 percent of companies use user-generated content as part of their marketing strategy.

In this article, we have explained the ten best ideas to create wonderful user-generated content for your brand. Let’s start without any further ado.

  1. Content From Social Media Platforms
    In the year 2020, there are 3.81 million people actively using social media around the globe. That is the reason social media content matters. Whenever users look at the content on social media that is posted by an individual, then they may be influenced by their content. Perhaps, it can be used to gain more customers or followers on your social media platforms.

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Generally, social media platforms help the brand to generate content for your users. Any user content that promotes your brand on the social media platform is the user-generated content for your business. When users create and share content on social media, they get 28% higher engagement than a standard company post.

Furthermore, you can embed your social media feed on your website also. you can use the Social Stream Designer WordPress plugin that will integrate various social media feeds from different social media platforms like Facebook, Twitter, Instagram, and many more. With this plugin, you can create a responsive wall on your WordPress website or blog in a few minutes. In addition to this, the plugin also provides more than 40 customization options to make your social stream feeds more attractive.

  1. Consumer Survey
    The customer survey provides powerful insights you need to make a better decision for your business. Moreover, it is great user-generated content that is useful for identifying unhappy consumers and those who like your product or service.

In general, surveys can be used to figure out attitudes, reactions, to evaluate customer satisfaction, estimate their opinions about different problems. Another benefit of customer surveys is that collecting outcomes can be quick. Within a few minutes, you can design and load a customer feedback survey and send it to your customers for their response. From the customer survey data, you can find your strengths, weaknesses, and get the right way to improve them to gain more customers.

  1. Run Contests
    A contest is a wonderful way to increase awareness about a product or service. Contest not just helps you to enhance the volume of user-generated content submissions, but they also help increase their quality. However, when you create a contest, it is important to keep things as simple as possible.

Additionally, it is the best way to convert your brand leads to valuable customers. The key to running a successful contest is to make sure that the reward is fair enough to motivate your participation. If the product is relevant to your participant, then chances are they were looking for it in the first place, and giving it to them for free just made you move forward ahead of your competitors. They will most likely purchase more if your product or service satisfies them.

Furthermore, running contests also improve the customer-brand relationship and allows more people to participate in it. It will drive a real result for your online business. If your WordPress website has Google Analytics, then track contest page visits, referral traffic, other website traffic, and many more.

  1. Review And Testimonials
    Customer reviews are a popular user-generated content strategy. One research found that around 68% of customers must see at least four reviews before trusting a brand. And, approximately 40 percent of consumers will stop using a business after they read negative reviews.

The business reviews help your consumers to make a buying decision without any hurdle. While you may decide to remove all the negative reviews about your business, those are still valuable user-generated content that provides honest opinions from real users. Customer feedback can help you with what needs to be improved with your products or services. This thing is not only beneficial to the next customer but your business as a whole.

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Reviews are powerful as the platform they are built upon. That is the reason it is important to gather reviews from third-party review websites like Google review, Facebook review, and many more, or direct reviews on a website. It is the most vital form of feedback that can help brands grow globally and motivate audience interactions.

However, you can also invite your customers to share their unique or successful testimonials. It is a great way to display your products while inspiring others to purchase from your website.

  1. Video Content
    A great video is a video that is enjoyed by visitors. These different types of videos, such as 360-degree product videos, product demo videos, animated videos, and corporate videos. The Facebook study has demonstrated that users spend 3x more time watching live videos than normal videos. With the live video, you can get more user-created content.

Moreover, Instagram videos create around 3x more comments rather than Instagram photo posts. Instagram videos generally include short videos posted by real customers on Instagram with the tag of a particular brand. Brands can repost the stories as user-generated content to engage more audiences and create valid promotions on social media.

Similarly, imagine you are browsing a YouTube channel, and you look at a brand being supported by some authentic customers through a small video. So, it will catch your attention. With the videos, they can tell you about the branded products, especially the unboxing videos displaying all the inside products and how well it works for them. That type of video is enough to create a sense of desire in the consumers.

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#how to get more user generated content #importance of user generated content #user generated content #user generated content advantages #user generated content best practices #user generated content pros and cons

Recommendation System: Collaborative Filtering

This article contains detailed implementation steps of Collaborative Filtering in python without any external libraries from scratch.

As the name suggests, this is a part 2 of the Recommendation System article where part 1 focuses over content based recommendation system, this article will focus over collaborative filtering approach i.e. Harnessing quality judgments of other users.

_The main idea in collaborative filtering revolves around predicting the rating of an item for user __X _based on the ratings given by a set of similar users.

Let us try to understand this definition, here ‘similar users’ refer to a set of users that have similar likeness and dis-likeness as user X’s. So, for example if user x has disliked an item a then the similar users must also dislike the item _a _and vice versa. Although, the strength of their similarity depends upon the ratings provided by the users. Fig 2 describes this process where we are trying to predict likable items for Mr. A.

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Fig. 2

Once we have a set of users that have rated the items in a similar way as of user X then we can start predicting the ratings for the items that have yet not been used by the user _X _and the items with highest ratings will be recommended to the user X. This approach is also known as user-user based collaborative filtering as we are matching user profiles and not item profile in that case it would be item-item based collaborative filtering.

Sounds simple! Well let’s try to implement it.

Data Set:

I am using the same data set as used in part 1 i.e. anime dataset from Kaggle. The data-set contains two files rating.csv having user’s rating for different anime so total 3 columns and anime.csv which is containing details for all the anime like name, type, average ratings, etc. There are total 12,294 unique anime, 73,516 unique users and 7,813,737 total ratings.

In this approach we will mostly use the rating.csv file i.e. the file containing ratings given by each user to some anime. There are missing values -1, for user indicating that the user has watched this anime but has not rated it. The global average rating is 7.8.

Implementation Steps:

The implementation is mainly divided into 3 tasks:

**Task 1: **To calculate a set of similar users as of user X. And for calculating the similarity between two users we have used Pearson Correlation Coefficient between user x with rest of all users. Once done, it will return a list of N most similar users.

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Fig. 2 (Pearson Correlation Coefficient)

**Task 2: **After getting a list of similar users as user X we can predict the ratings of the anime that the user _X _has not watched but similar users from the set N have watched.

#anime #big-data-analytics #recommendation-system #collaborative-filtering #machine-learning #deep learning

Janae  Haag

Janae Haag

1594742100

Beer Recommendations using Collaborative Filtering with Neo4j

In this post, I’ll outline how to use a Neo4j graph database to generate user recommendations for a data set consisting of users, products, and user ratings for those products.
For my data set I’m using a database of 30,000 different beers (pulled from brewDB’s open API), and 100 users (I asked facebook friends to rate some beers).

#neo4j #recommendation-system #collaborative-filtering #graph-database #database