If big data are at the root of all megatrends, data science refers to its study. The data-driven predictions circle around reality. In short, the data tell the truth what you search about. Even, you can come across someone’s intentions accurately, which are hard to trace. This is why diverse industries have developed a common love for the data analytics.
Why do users require recommendations?
Being a part of the digital age, the industrialists cannot leverage on the word-of-mouth publicity. The seller mostly interacts virtually, which decreases the chance of being recommended in-person. Undoubtedly, the advent of e-Commerce has changed the scenario. But, it cannot decline the power of recommendations.
However, you cannot directly speak favourably of likeable items online. But, you can check a series of choices, mobbing your favourite apps or websites. Their recommendation stems through the deep learning that the machine experiences through users’ past web/app experience. It shows how amazing the AI is pushing efforts round o’clock to suggest you. In the nutshell, it proves substantially a hit formula to spike cross selling.
What if the user is new to that app?
It’s certain that the AI cannot map his web journey, as it is his first impression. But, the recommender system can measure his interest and needs. On that basis, it comes up trumps, creating a positive impact.
Let’s get through what it is.
What is a recommendation engine?
It is the AI-based machine intelligence, which taps users’ web or app journey. Then, it filters the data according to different use-cases based algorithms. This is how it communicates with the past behavior of the customers. Post analysis, it recognizes to and pairs up products or services that they might be likely to buy. You can practically witness it in the recommendations just below the product that you browse in particular on Amazon, for example.
How does data science help to get recommendations on LinkedIn?
As aforementioned, the AI substantiates suggestions through these mechanisms:
- Identifying the most popular titles or products amongst all users and putting them into suggestions
- Segmenting customers by determining their preferences and then, putting suggestions as per segment
But, both mechanisms fall flat in tailoring personalised recommendations. The first scenario will put forward the similar titles or products to pick up from, which does not fit the preferences of all users. On the other hand, the second case will become critical, as the online traffic appreciates. The segmentation and preferences will superfluously exaggerate. Thereby, their clustering will be an uphill climb.
Only personalised method can sail you across these drawbacks. The web researchers extract a part of or complete database from the existing data management system to do so. Thankfully, LinkedIn follows this personalised method. Here are the steps that it follows to get suggestions per user.
- Collection Data: LinkedIn builds recommendations around the data, which are extracted through implicitly and explicitly.
- Explicit data: When the users provide their information intentionally, it is known as explicit data, for example-feedback, reviews and profile information on LinkedIn.
- Implicit data: It is contra to explicit data. The users provide their information. But, their intention differs. For example-a user subscribes to an app by inputting email Id, phone number and location etc.. The app owner will collect that implicit data for customer analysis and determining their app experience.
- Store Data: The size of data determines the recommendations profiling. The more data you have, the more factors you would come across for recommending. Mostly, the storage includes a standard SQL database, a NoSQL database etc..
- Filtering Data: Once the data is captured, this engine defines tags to filter the information. Certainly, AI-supported algorithms automate it. Let’s say, Netflix streams series or movies in diverse genres, such as thriller, action, drama, romance and horror. Its recommendation engine will cluster users according to what they watch for. This is how it drills their behaviour to return the synonymous choices. If A has watched Spiderman, it will determine its genre, i.e. action. Afterwards, it maps the consistency in that genre. Finally, it flocks the suggestion box with the movies in the genre that he has watched a lot for in the past.
Let’s get through the rundown of different types of filtering:
- Content-Based Filtering: This algorithm identifies the content similar to what a particular user has liked in the past. For instance, you had been hitting like tab of ‘Data Entry Tips and Tricks’, ‘Data Entry Tutorials’ and ‘Data Entry Shortcuts’. This engine will filter ‘Data Entry’ to set it as a tag under Recent and Followed Hashtags as recommendations.
- Collaborative Filtering: The collaborative filtering algorithm deals with filtering similar and dissimilar interests. Let’s say, a user searches for the ‘latest jobs’, ‘research companies’ and ‘outsourcing companies’ on LinkedIn. Another user explored ‘latest jobs’, ‘finance companies’ and ‘outsourcing companies’. This algorithm will consider the ‘latest jobs’ and ‘outsourcing companies’ as a user behavior and put them under recommendations.
Lastly, the engine works on evaluation metrics. It uses various methods, like Recall, Precision, Root Mean Squared Error, Mean Reciprocal Rank, MAP at K and Normalized Discounted Cumulative Gains. These ways eventually compose tags or recommendation to easily filter and catch the intended content.