Smartphone Recommendation System – With the increasing popularity of smartphones, personal assistants, and apps, the need for better on-device recommendation mechanisms is felt.
Recommendation engines are used by many famous companies such as Amazon, Netflix and Spotify. Currently, they are usually based on four technologies: collaborative filtering; filter based on content; demographic filter; and knowledge-based filtering. Each of these models has specific performance weaknesses that often result in poor user-relevant scores.
Smartphone Recommendation System
Mobile phones contain textual data that can improve the quality of recommendations. For example, user data in the form of calendars, text messages, email, web browsing history, and application usage logs. Using these data to improve recommendations raises several issues, including data privacy concerns, data storage costs, and communication costs.
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New developments have shown that RDFox can be used to build on-device context-aware recommendation engines that increase user satisfaction without compromising data privacy. An academic paper published at the Semantic Web International Conference (November 2020) written by Samsung researchers and Oxford University professors Ian Horrocks and Boris Motic (co-founders of Oxford Semantic Technologies) shows how. This article summarizes their approach.
An on-device knowledge graph is convenient because it avoids data security concerns because the data is only visible to the device (i.e., it is never transferred to a remote server, where it can be hacked or improperly monetized). In addition, real-time local data can be used to provide context-aware recommendations. This method is more suitable than machine learning techniques because it requires less computing resources and less data modeling.
RDFox is a high-performance knowledge graph and semantic inference engine. It was originally developed to run on servers. However, this study explored the possibility of a version that would work on Android devices. RDFox is suitable because:
Researchers populate the RDF knowledge graph with a variety of data and knowledge, including: user textual data, extracted using pattern-based techniques; Domain knowledge, extracted by demographic analysis. and basic / common sense knowledge from Wikidata, ConceptNet and SenticNet. These data sources are integrated using rules to provide a rich knowledge graph that can be accessed using SPARQL queries.
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RDFox uses reasoning to provide appropriate recommendations based on user needs and preferences and contextual information. These recommendations change gradually as data changes. That is, if a reference is no longer valid, RDFox will remove it without having to recalculate all references.
To build a successful on-device text-aware recommendation engine, several challenges must be overcome, including: Storage. performance, including speed, accuracy and scalability; communication costs; and technological challenges
The research showed that the on-device version of RDFox can handle large amounts of data, typically taking a few milliseconds to provide results, even for incremental inference tasks. The system can also provide users with explanations for recommendations
The proposed system faced the technical challenge of producing the system on the device, and the ability to install RDFox on an Android phone is a significant improvement.
Crop Recommendation · Github Topics · Github
During a study to test the accuracy of a context-aware system’s recommendations, a sample group rated the recommendations 73 percent more accurate than an alternative recommendation engine. This shows that this recommendation method is effective and can improve the user experience.
It is clear that on-device inference systems have a place in the future development of recommendation services. Samsung plans to use this research to improve its smartphone recommendation services for music, videos, articles, apps, text greetings and more. This study shows that on-device thinking with RDFox can provide accurate and personalized recommendations without compromising users’ data rights.
The Oxford Semantic Technologies Support Team started work on RDFox in 2011 at Oxford University’s Department of Computer Science with the belief that high-performance flexible reasoning was a possibility for data-intensive applications without compromising the accuracy of the results. RDFox is the first market-ready knowledge graph designed from the ground up with thought in mind. Oxford Semantic Technologies is part of the University of Oxford and is backed by leading investors including Samsung Venture Investment Corporation (SVIC), Oxford Sciences Innovation (OSI) and Oxford University Investments (OUI). The author is proud to be a member of this team. Why recommender systems are the most valuable application of machine learning and how machine learning-based recommenders are enhancing almost every aspect of our lives.
Look at your week: A machine learning algorithm determines what songs you want to listen to, what food to order online, what posts you see on your favorite social networks, as well as the next person who you want to communicate with, series or movies you want to watch, etc…
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Machine learning is already driving many aspects of our lives without necessarily realizing it. All the programs mentioned above are driven by one type of algorithm: recommendation systems.
In this article, I’ll go deeper into all the aspects that go into building a successful referral system. The length of this article is getting a bit out of control, so I decided to split it into two parts. This first part will include the following:
In this article, I will use examples from companies that have built the most extensive systems in the last few years, including Airbnb, Amazon, Instagram, LinkedIn, Netflix, Spotify, Uber Eats, and YouTube.
Harvard Business Review made a strong statement, calling Recommenders the most important algorithmic difference between born-digital businesses and legacy companies. HBR also explained the virtuous business cycle they can create: the more people who use a company’s referral system, the more valuable they become, and the more valuable they are, the more people use them.
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We are encouraged to look at referral systems, not as a way to sell more online, but as a renewable source of
. If we look at the image above, we can see that many old companies also have a large number of users and thus thousands of data. The reason their virtuous cycle hasn’t grown like Amazon, Netflix, or Spotify is because they lack knowledge of how to turn user data into actionable insights that can be used to improve their products or services.
A look at Netflix, for example, shows how critical this is, as 80% of what people watch comes from some sort of recommendation. In 2015, one of their articles reads:
If we look at Amazon, 35% of what customers buy on Amazon comes from product recommendations, and on Airbnb, search rankings and similar ads drive 99% of all booking conversions.
What Content Based Filtering Is & Why You Should Use It
Now that we’ve seen the tremendous value companies can get from Recommender Systems, let’s look at the kinds of challenges they can solve. Generally, tech companies try to recommend the most relevant content to their users. It can mean:
Problem formulation is critical here. Most of the time, companies want to recommend content that users are likely to enjoy in the future. A reformulation of the problem, as well as algorithmic changes from recommending “what users are likely to view” to “what users are likely to view most.”
“Amazon researchers found that using neural networks to generate movie recommendations performed much better when they sorted the input data chronologically and used it to predict future movie preferences over a short period (one to two weeks).”
The “classic” and still widely used approach to collaborative filter-based recommendation systems (used by Amazon, Netflix, LinkedIn, Spotify, and YouTube) uses user-to-user or case-by-case relationships to find similar content. I won’t go into the inner workings of this, as there are plenty of articles on the subject — like this one — that explain the concept well.
What’s A Recommender System?
Implicit Data: Information not intentionally provided, but collected from existing data streams (such as search history, order history, clicks, engagement accounts, etc.)
It mainly consists of thematic features. In the case of YouTube, this is video metadata such as the title and description. For Zillow, this could be, for example, the home’s zip code, city area, price, or number of bedrooms.
Other data sources can be external data (for example, Netflix can add external data features about an item such as box office performance or critic reviews) or data generated by experts (Pandora’s Music Genome Project from It uses human input to apply a value for each song (to each of about 400 music features).
The key insight here is that obviously having more data about your users will inevitably lead to better model results (if applied correctly), however, as Airbnb explains in its three-part journey for Creating a ranking model for Airbnb experiences shows you can already achieve a result. More with less data: The Airbnb team has already improved bookings by +13% with just 500 experiences and 50,000 training data.
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The bottom line is this: don’t wait until you have big data, you can do a lot with small data to help your business grow and improve.
We often associate recommender systems with collaborative filtering only. This is fair, because in the past this has been the method for many companies that have implemented successful systems in practice. Amazon was probably the first company to