Why A Product Search Engine And A Recommendation Engine Can’t Replace Each Other
There is no denying that everyone who spends time shopping online has seen both search engines and recommender systems several numbers of times and the differences between both the engines seem fairly obvious. A search engine can be spotted easily — it is a query box where you type in what you’re looking for and the system shows a list of results. On the other hand, you also see some products has just appeared on your screen that is relevant to you, but you haven’t requested for it; that’s is the magic of a recommendation engine. But what makes a product search engine different from a recommendation engine? And where exactly deep learning, machine learning, and artificial intelligence come into the picture? How Both Engines Work Together People tend to buy products recommended by others and in this digital age, almost every online shop utilises some sort of search and recommendation engine that not only shows you products you look for but also products you might like. Talking about recommendation engines, they are basically tools that use algorithms and data to recommend the most relevant items to a particular user. But how both the search engine and recommender systems work together. Simply put, when you go to a shop and ask the counter guy for a particular product, he takes you to the exact rack and shows you the product; this is a search engine. But the counter guy not only shows that product but also the related ones which you could buy; this is recommendation engine. The Amazon Recommendation Engine With so many online shopping platforms, we have seen a lot of recommendation systems that differ from each other — in terms of both characteristics and quality. To better understand a recommendation engine, let’s take the Amazon product recommendation engine as an example. It is a more sophisticated recommendation system that is built in-house. The engine not only recommends products but also recommends add-ons based on the products the visitor is about to buy. And it does this based on what others have bought along with that item. Amazon recommendation engine is without a doubt a powerful tool to increase average order values. Amazon makes use of browsing history of users to always keep those products in the eye of the customer. The system also uses ratings and reviews of customers to display products with a greater average in the recommended and bestselling option. The bottom line is Amazon mostly focuses on making its visitors buy a package rather than one product. How Product Search Engine And Recommendation Engine Differ Conventional product search engines don’t use any kind of sophisticated algorithm to run a search. Most of the online store is equipped with a basic search engine that pulls out results based on product titles, descriptions and category structure, auto-correct, fuzzy-match and recognises synonyms. On the other hand, a recommendation engine not only shows results but also recommends similar products. Unlike input in a conventional search engine, a recommender system takes products as input. Therefore, the product being the input, the algorithm of the engine creates a search query to find other products — by the same brand, within the same category, or with similar keywords — that the visitor might want to buy. Now, the search engine has its own benefits. Even though a recommendation engine provides additional results along with search results using customer behaviour data, but when it comes to some of the big-time online shopping enthusiasts, they always have something specific in mind and look for exact results that a dedicated product search engine can do. How Machine Learning And Deep Learning Are Making An Impact Over the past couple of years, Machine Learning has been on the rise, invading numerous verticals. And as it has stepped into the search and recommendation space, algorithms today are smarter and able to make great strides when it comes to making use of user behaviour data. Also, deep learning is another factor that is making a great impact. It is making search engines to learn and understand the relationships between different products. Today, a deep learning search engine is not only delivering search results but also a showing list of other products that have no relation to the actual search. Wrapping Up Shopping is a necessity of every human being, and with the rise of online shopping, people are relying on online stores more than ever — whether it’s about clothing or groceries. With the growing amount of information on the internet and with a significant rise in userbase, it is becoming imperative for online stores to search, map, and provide relevant results. And for that, a top-tier search and recommendation engine are necessary.
There is no denying that everyone who spends time shopping online has seen both search engines and recommender systems several numbers of times and the differences between both the engines seem fairly obvious. A search engine can be spotted easily — it is a query box where you type in what you’re looking for and […]
The working of a search engine and a recommendation engine and why they caannot replace each other.
["Amazon", "Deep Learning", "Machine Learning"]
["Go", "artificial intelligence", "machine learning", "programming_languages:R", "AI", "Amazon", "Machine Learning", "recommendation systems", "RAG", "Git", "deep learning", "Deep Learning", "R"]
["AI", "artificial intelligence", "machine learning", "deep learning", "RAG", "recommendation systems", "R", "Go", "Git", "programming_languages:R"]
Harshajit is a writer / blogger / vlogger. A passionate music lover whose talents range from dance to video making to cooking. Football runs in his blood. Like literally!
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