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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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Graph-Based Recommendation System With Milvus

Background

A recommendation system (RS) can identify user preferences based on their historical data and suggest products or items to them accordingly. Companies will enjoy considerable economic benefits from a well-designed recommendation system.

There are three elements in a complete set of recommendation systems: user model, object model, and the core element—recommendation algorithm. Currently, established algorithms include collaborative filtering, implicit semantic modeling, graph-based modeling, combined recommendation, and more. In this article, we will provide some brief instructions on how to use Milvus to build a graph-based recommendation system.

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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.