Nowadays, people did not even realise that their online search history contributes to the recommender system algorithms. For example, you searched a dress but did not buy it, that dress will appear on your next online search.

1. BRIEF GUIDE OF THE RECOMMENDERSYSTEM
Gone are the days when people relied upon their friends, family, or experts to
recommend or advisethem on whatthey should read, eat, or watch. But with the
advent of the internet, people now rely upon the taste-making algorithms.
Nowadays, peopledid not even realise that their online search history contributes
to the recommender systemalgorithms. For example, you searched a dress but
did not buy it, that dress will appear on your next online search.
The recommender systemanticipates the user’s preferencebased on his search
history. With a plethora of content available online, the recommender system
helps in segregating and filtering the relevant content. Recommender systemnot

2. only improves the user’s onlineexperience but also enhances CTRs, conversions,
revenue and other metrics.
How does a Recommender System work?
There are four phases in the recommender system:
1. DATA COLLECTION
2. DATA STORAGE
3. DATA ANALYSIS
4. DATA FILTRATION
1. The data collection is the firststep in the recommender system. Thereare
two ways of procuring the data i.e explicit and implicit. A business can
collect implicit data from search history, cartevents, search log, and
pageviews. You can drive explicit data from the user’s rating, review and
feedback.
2. You can useany databasefor collecting data like a standard SQL database,
NoSQL database or any objectstorage. First determine factors like ease of
implementation, data management, management, and portability. A well-
managed databasestreamlines the entire process and concentrates on
suggesting recommendation to users.
3. For filtering similar user engagement data, you can use simple analysis
method. For providing an immediate recommendation, you should bellow
recommendation system.
The real-time systemprovides in-the-momentrecommendation by
analysing the stream of online activity, as it created.

3. Batchanalysis accumulates enough data periodically to extract relevant
information. For instance, use an email systemfor sending newsletters.
Near-real-time-analysis collectdata of every second and minute and offer
recommendation in the samebrowsing session.
4. Finally, filter all the relevant data to offer recommendations to the users.
You can chooseany below algorithms for that;
Content-basedalgorithms system recommend the productbased on
consumer’s onlinelikes, reviews and ratings.
Cluster algorithms recommend theproductirrespective of consumer’s
online activity.
Collaborative algorithms recommend thecontent or productbased on
other user’s online activity.
Ways for implementing the recommending system ina business:
Any business which does not have data storagecapacity can use online
frameworks likeHadoop, Spark to processed the dataset faster and assuagethe
dependability on one machine.
At last, use the MapReduce programming model for processing the data sets by
running the algorithm in the distributed file system. Hence, every business should
develop its recommendation systemby using open sourcetools.

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