With the beginning of web era, there has been an information overload over the internet which often makes it exhaustive for the user to get the relevant information. This issue is resolved by search engines like Google, Yahoo and many more, however, even they fail to provide personalized data. So, to additionally filter the data we need a recommendation search engine. Recommendation systems are software and techniques, designed with an objective to provide a useful and sensible recommendation to users for items or products that might interest them. Recommendation system typically does not use an explicit query, instead analyzes the user context and user profile, i.e., what the user has recently purchased or read. Then the recommendation mechanism provides the user with one or more specification of objects that may be of interest.
Over the past two decades, the Internet has emerged as the mainstay for online shopping, social networking, e-mail and many more. Corporations also consider the Web as a potential business accelerator. Thus, we see a huge volume of transactional and interaction data generated by the Internet every day. It becomes a herculean task for the user to find the relevant information. A recommendation search engine system provides a solution for this by allocating suggestions for the items to be of use to a user. Recommendation search engine systems have mathematical roots and are more akin to artificial intelligence (AI) than any other IT discipline. A recommendation search engine system learns from a customer’s behavior and recommends a product in which users may be interested.
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- Google (US),
- IBM (US),
- Microsoft (US),
- SAP (Germany),
- Salesforce (US),
- HPE (US),
- Oracle (US),
- Intel (US),
- AWS (US),
- Sentient Technologies (US).
The prominent players keep innovating and investing in research and development to present cost-effective offerings. Merger and acquisitions among various players are changing the market structure. For instance, Google has acquired Jetpac, makers of an app that recommends destinations based on an analysis of publicly shared Instagram photos. The technology works automatically, extracting information from large numbers of publicly available photos instead of relying on curation or other manual processes.
According to MRFR, The global Recommendation Search Engine market is expected to reach approximately USD 5,900 Million by 2023 growing at a ~40% CAGR over the forecast period 2018-2023.
The geographical analysis of Recommendation Search Engine market is studied for North America, Europe Asia Pacific and the rest of the world.
North America is expected to dominate the Recommendation Search Engine market during the forecast period as many organizations are shifting towards new and upgraded technologies with the increasing adoption of digital business strategies. Also due to the rise in the focus of the companies to enhance consumer experience is major driving for the growth of Recommendation Search Engine Market. Asia Pacific is expected to grow at a faster rate due to rapid digitalization and the increasing presence of over the top players (OTT).
On the basis of type, the market is segmented into Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation. Collaborative Filtering is expected to grow at the highest CAGR during the forecast period. This model uses the collaborative power of the ratings provided by multiple users to make recommendations. Collaborative filtering approach doesn’t need a representation of items in terms of features, it is based only on the judgment of the participating user community which is an advantage. Several industries such as Retail, Media & entertainment, and others have deployed recommendation systems powered by AI and Big data for various applications such as personalizing campaigns.
On the basis of Technology, market is segmented into Context-Aware and Geospatial Aware. Context-aware technology is expected to dominate the market as it helps in giving diverse and accurate recommendations to the user. The contextual information includes the location of the user, Identity of people around, date, season, temperature etc. For instance, a website may recommend songs to a user by asking the current mood of the user.
On the basis of Application, market is segmented into Personalized Campaigns and Customer Discovery, Product Planning, Strategy and Operations Planning, Proactive Asset Management. Personalized campaigns and customer discovery application is expected to account for the largest market size during the forecast period. The more the recommendation system
knows about user’s profile, the better it can help to provide customized search results, recommendations or ads.
On the basis of Deployment Mode, market is segmented into On-cloud and On-premise. The Cloud deployment mode is expected to dominate the market due to high adoption of cloud technologies by SMEs as it is simple, efficient & cost effective.
On the basis of End Users, the market is segmented into Media and Entertainment, Retail, Banking, Financial Services, and Insurance, Transportation, Healthcare, and others. Retail Sector is expected to be a strong contender in Recommendation search engine market.
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- AI recommendation engine software and platform providers
- Training and consulting service providers
- AI System integrators
- Recommendation Search Engine vendors
- Government Agencies
- Managed service providers
- Research organizations
- Value-added Resellers (VARs)
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