The emergence of the internet has also created high information overload which is often exhaustive for the user to search for relevant information. Such issues are generally resolved by search engines such as Yahoo, Google, and others. However, these search engines fail to showcase personalized results to the users. Hence, in addition, to filter data, there is a need for recommendation search engine. Recommendation systems refer to software and technology which is designed with the objective of providing sensible and personalized recommendations to users for several items and products that they might be interested in. A recommendation search engine generally does not require an explicit query insertion by the user. It analyzes the context of the user, along with their past browsing and shopping habits and their profile. This information is then used by the algorithm in place to provide the user with recommendations.
The global recommendation search engine market is anticipated to upscale at the fastest rate over the forecast period, as per the latest Market Research Future (MRFR) report. The global recommendation search engine market is anticipated to witness a skyrocketing CAGR of 40% by the end of the review period. Ascension noted in the market can be accredited to the emergence of digitization and the need to enhance consumer experience. For instance, the recommendation system in Youtube delivers a personalized set of videos that are relevant to the users recent search or recently watched videos.
Another factor driving the global recommendation search engine market is the upsurge in demand for the analysis of big data. A recommendation system retrieves relevant information about the user in an automated manner to achieve long-term business objectives. One of the factors hampering market growth during the forecast period is rising privacy concerns on behalf of the consumers. Along with that, the possibility of intrusive marketing is also causative of restrictions on market growth.
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The global recommendation search engine market is segmented on the basis of type, technology, application, deployment, end-user, and region. By type, the market segments include into collaborative filtering, content-based filtering, and hybrid recommendation. Based on technology, market segments include context-aware and geospatial aware. The context-aware segment is sub-segmented into machine learning, deep learning, and natural language processing. Based on application, the global market is examined for the segments of personalized campaigns and customer discovery, product planning, strategy, and operations planning, proactive asset management. Based on deployment, the market is segmented into on-cloud and on-premise. Based on end-user, the global recommendation search engine market is segmented into media and entertainment, retail, banking, financial services, and insurance, transportation, and healthcare.
The global recommendation search engine market is studied for the regions of North America, Europe, Asia Pacific, and Rest of the World. Among these regional segments, market in North America dominated the global market at the beginning of the forecast period. This can be credited to the increasing inclination towards new and upgraded technologies owing to the increasing adoption of digital business strategies. Moreover, rising focus of companies towards the enhancement of customer experience is also expected to aid market growth in the region.
Asia Pacific is projected to upscale at the fastest growth rate over the forecast period. This can be accredited to the rapid penetration of digitization in the region, along with rising expansion activities undertaken by various players.
Prominent players in the global recommendation search engine market, as profiled in the latest MRFR report, include Google (US), IBM (US), Microsoft (US), SAP (Germany), Salesforce (US), HPE (US), Oracle (US), Intel (US), AWS (US), and Sentient Technologies (US).
August 2019: LANDR recently added one million royalty-free sounds to its samples database and launched the world’s first AI-powered Sample Recommendation Service. LANDR is a creative platform for musicians.
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