10 RECOMMENDATION ENGINES THAT 10 POPULAR BRANDS NEED TO HAVE!
By Kamal Jacob
In this digital world, we all have so many choices to choose from! Let’s say you are looking for a great book to read but not sure of what type, you may end up spending a lot of minutes on the Internet searching for right books. You also may seek personalized recommendations.
Power of recommendation engine is used by most of the online platform and mainly e-commerce and entertainment platforms. Flipkart, Google, Netflix, and all leading companies use recommendation engines. Let us have a look at 10 recommendation engines which can help businesses of any type and popular brands.
A recommendation engine predicts what a user may or may not like among a list of given items. They help users discover products or content that they may prefer or like to have. Recommendation engines have become an unavoidable part of services such as YouTube, Amazon, and others.
Recommendation engines analyze items liked by the user and determine what else the user may like. Engines make use of similarity index between users and recommend items to them accordingly. To make this possible, the engines make use of likes and dislikes of other similar users.
Dressipi – Up your fashion game:
Dressipi is one of the leading fashion prediction platforms. Dressipi Engine builds models which represent customer preferences, behavior, and future buying patterns. It processes hundreds of data points for analysis and recommendation. It uses machine learning and expert domain knowledge to recommend outfits for occasions or based on moods and interests.
Dressipi recommendation engine can be used by Meheraan which provides designer wear clothing for the bride and her friends. Based on the bride’s preferences the engine can build and show recommendation on what kind of dress she can wear on her special day.
Meheraan can also use this recommendation engine to recommend new clothing based on customer preferences and their purchases in the past. Such as someone likes to buy trendy clothes every few months, the engine can recommend them clothing based on latest trends.
Sajari is an online customized matching engine. It does profile matching and real estate property matching. In profile matching, it helps people who are looking for a date or partner having similar interests, likes and dislikes.
This kind of recommendation engine can be utilized via dating apps and dating websites such as Tinder, OKCupid, happn and many others. As of now some apps shows profiles based on location, age, and other parameters but if these platforms make good use of such kind of recommendation engine then match-making process will become much easier and very efficient.
Gyde – Streaming media recommendations:
Gyde is a streaming recommendation engine. If an online platform has a custom database of users then the platform can use Gyde API. The Gyde API will integrate and match the custom database to legally available streaming options or video on demand services such as Netflix, iTunes, Google Play and others.
This kind recommendation engine can be used by online services such as Flickstime which can recommend upcoming or latest movies to users based on the movies checked by them in the past.
Criticker – Game recommendations:
Criticker is an online game recommendation platform. User will need to rate ten games based on the score of 0 to 100. If a user gives 0 to a game it means that the user doesn’t like the game or the game is least liked by the user.
After the user rates a total of 10 games between 0 to 100. The recommendation engine will take the score and recommend new games to the user based on this rating and preferences. This kind of recommendation engine can be utilized by an online gaming platform and gaming platforms such as Steam and Xbox Live.
Movielens – Movie Recommendation:
MovieLens is a movie recommendation engine. It helps users find movies they like to watch. The engine recommends new movies to users based on the rating given by them to certain movies. The engine then builds a custom profile and recommends movies to users based on the custom profile of each user.
These platforms use the preferences of the user and suggest him or her new movies to watch based on the movies watched by them in the past
This kind of recommendation engine can be used by PVR Cinemas. On their online platform, they can integrate such a recommendation engine which sends movie recommendation email or SMS to the user based on the movie ticket booking done by the customer.
Mr. DLib is a recommendation engine for academic organizations. It is highly useful for college students, professors at college and experienced people who are pursuing their Ph.D.
Let’s say if a student is making one assignment or preparing a research paper and he/she is reading some academic articles and the recommendation engine will suggest the person more academic articles to read based on the topic being searched or read by that person.
This engine can be used by Google Books, academic institutions such as Harvard University, Stanford University, and other universities. It can help students and researchers a lot by helping them find relevant articles and will make the process of research quicker.
Metisa is a recommendation engine built for consumer product-based businesses to increase their sales. It also helps in improving customer retention by helping businesses to do winback campaign. For businesses, Metisa gives them a recommendation on how they can grow their business by telling what they should do next.
For consumers, it is a recommendation and personalization engine. As visitors browse the website, it generates immediate recommendations. Based on consumer browsing behavior, it can personalize an entire page and also does cross-selling and up-selling product recommendation. It also helps the brand to winback customers who get disengaged from the brand.
This recommendation engine can be used by products website such as Zalora, to personalize their entire website browsing experience.
TasteDive is a slightly different kind of recommendation engine. It recommends music, movies, TV Shows, Books, Games, and Podcasts to users. In this article, we are looking for its music recommendation and book recommendation aspects.
The first time user will enter his or her preference and taste in terms of movies, TV shows, books, and games. Based on the selection and rating shared by the user, TasteDrive will recommend new movies, new books, new games, and the latest movies as well.
This kind of recommendation will be useful to brands and online platforms such as Steam and GoodReads.
Segmentify is an excellent recommendation engine for personalized product recommendations and personalized emails. This recommendation engine can be used by products websites such as Loveknits.
Loveknits is selling handcrafted eco-friendly products. Loveknits platforms can use this recommendation engine to send personalized emails and messages to users with personalized product recommendations based on the blog articles read by them and purchase history.
Recombee - personalized experience through recommendations
Recombee is a recommendation engine which specializes in personalizing the experience. It can give recommendations which are not limited to product/item but also provide recommendations for gaming, e-learning and job boards. It also does content recommendations to users on newly released content based on each user’s preference.
This recommendation engine can be used by products websites such as Yogasvi. Content recommendation aspect of the recommendation engine can be utilized by news and blog website such as GSMArena.
Too long; didn’t read? Let us summarize!
Recommendation engines analyze items liked by the user and determine what else the user may like. They help users discover products or content that they may prefer or like to have. Recommendation engines are being used by leading brands and platforms such as YouTube, Amazon, Flipkart and many others.
Brand use recommendation engines to recommend products and content such as movie, song or a video based on the preferences of the customer. There are many fantastic recommendation engines out there such as Dressipi which can higher improve customer experience.
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We hope that this article helped you to know how 10 new recommendation engines and how those can be relevant and useful to some popular brands. Do you know about any other recommendation engine which can be useful to any brand? Let us know your thoughts in the comment section.
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