Question

difference between search engine and recommended syserem

Answer 1

 In a search engine, the user knows what he is looking for, and he makes the query ! For instance, I might wonder if I should go to see a movie, and search informations about it, like actors and directors.

2/ In a recommender system, the user isn't supposed to know what we are recommending to her. We match her tastes with neighbours or wathever algorithm youme like, and find things that she would't have looked after, like a new movie !

One is more about information retrieval, while the other is more about information filtering and discovery.

Answer 2

While recommender systems tend to focus on solving problems such as:

Data sparsity: in order to have an effective system you usually need to have a large number of users that have interacted heavily with the website. This group of people will have a large influence in how the recommender engine worksCold-start: when fresh, the engine has no, or very little, behaviour data. Depending on the retailer, it can take months to accumulate enough behaviour data.Huge amounts of data

Search engines typically focus on things like:

Efficient indexing: used to quickly locate data without having to search every row in a database table every time a database table is accessedAttribute ranking rules: rank results by their expected relevance to a user's query using a combination of query-dependent and query-independent methodsFuzzy query matching: approximate string matches (e.g. “rmabo” vs “rambo”)Stemming: getting words to match each other even if they are not in the exact same form (e.g. run/runs/running/ran or cat/cats)Tokenization: the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens