Query Recommendation System in Social Networks
DOI:
https://doi.org/10.54060/jmss.2022.20Keywords:
Text Mining, Indexing, Stemming, Recommendation SystemAbstract
Recommender Systems are software which provides suggestions to the user accord-ing to his or her interest. These suggestions are related to supporting users in making their decisions, for example what to search, what to buy, what to listen, etc. Recommender systems are very important in online stores where there are a lot of items to buy. These recommender systems help user to find things according to their interest and buy them. There are a lot of techniques proposed for recommendation and used in commercial environments. People are thought to trust suggestions from friends more than those from websites that are similar to them [2]. As a result, it is helpful to feed a recommender system with the friends' ratings. However, social me-dia sharing websites' recommender systems have several difficulties, such as ranking the information from the user's friends as well, finding information from other sources in comparison to the user's immediate friends, and using metadata and context links for suggestion. In this research, an architecture based on profile-based crawling of social media sharing websites is proposed for query recommendation.
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