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Building an Intelligent News Recommendation System Inside Sohu News App

With 71% of Americans getting their news recommendations from social platforms, personalized content has quickly become how new media is discovered. Whether people are searching for specific topics or interacting with recommended content, everything users see is optimized by algorithms to improve click-through rates (CTR), engagement, and relevance. Sohu is a NASDAQ-listed Chinese online media, video, search, and gaming group. It leveraged Milvus, an open-source vector database built by Zilliz, to build a semantic vector search engine inside its news app. This article explains how the company used user profiles to fine-tune personalized content recommendations over time, improving user experience and engagement.

Recommending Content Using Semantic Vector Search

Sohu News user profiles are built from browsing history and adjusted as users search for, and interact with, news content. Sohu’s recommender system uses semantic vector search to find relevant news articles. The system works by identifying a set of tags that are expected to be of interest to each user based on browsing history. It then quickly searches for relevant articles and sorts the results by popularity (measured by average CTR), before serving them to users.

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How Graph Analytics Can Transform Your Business

Introduction

Your business is operating in an ever more connected world where the understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes. This is the main reason why graph databases have gained a lot of interest in the past few years and have become that fastest-growing database category. They offer powerful data modeling and analysis capabilities your business can use to easily model real-world complex systems and answer challenging questions previously hard to address.

What Is a Graph Database?

You might not be aware of it, but many of the services you use on a daily basis are powered by a graph database. Such examples include Google’s search engine, Linkedin’s connection recommendations, UberEats food recommendations and Gmail’s autocomplete feature. Simply put, a graph database is a data management system specifically engineered and optimized to store and analyze complex networks of connected data where relationships are equally important to individual data points. As a result, they offer a highly efficient, flexible, and overall elegant way to discover connections and patterns within your data that are otherwise very hard to see.

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How to Build a Collaborative Filtering Recommender Engine with Memgraph and Cypher

Introduction

A recommendation engine is a system that tries to suggest relevant items to users. These could be movies (e.g Netflix), products (e.g Amazon), flights (e.g Skyscanner), etc. Recommendation engines have become a key component in today’s online-first world and if engineered properly, they can help significantly increase revenue for commercial applications.

Although many different approaches exist to building a recommendation engine, in this tutorial we will be focusing on one of the most widely used ones, collaborative filtering. We will be using a movie dataset to build a simple movie recommender system leveraging Memgraph and Cypher.

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Building a Recommendation System Using Deep Learning Models

In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.

The recommendation systems can be classified into 4 groups: