Essays about: "collaborative filtering"
Showing result 6 - 10 of 94 essays containing the words collaborative filtering.
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6. A graph database implementation of an event recommender system
University essay fromAbstract : The internet is larger than ever and so is the amount of information on the internet.The average user on the internet has next to endless possibilities and choices whichcan cause information overload. Companies have therefore developed systems toguide their users to find the right product or object in the form of recommendersystems. READ MORE
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7. Predicting future purchases with matrix factorization
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This thesis aims to establish the efficacy of using matrix factorization to predict future purchases. Matrix factorisation is a machine learning method, commonly used to implement the collaborative filtering recommendation system. READ MORE
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8. GROCERY PRODUCT RECOMMENDATIONS : USING RANDOM INDEXING AND COLLABORATIVE FILTERING
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The field of personalized product recommendation systems has seen tremendous growth in recent years. The usefulness of the algorithms’ abilities to filter out data from vast sets has been shown to be crucial in today’s information-heavy online experience. READ MORE
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9. Recommender system for IT security scanning service : Collaborative filtering in an error report scenario
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Recommender systems have become an integral part of the user interface of many web applications. Recommending items to buy, media to view or similar “next choice”-recommendations has proven to be a powerful tool to improve costumer experience and engagement. READ MORE
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10. Improving Recommendation Systems Using Image Data
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Recommendation systems typically use historical interactions between users and items topredict what other items can be of interest to a user. The recommendations are based onpatterns in how users interact similarly with items. READ MORE