Essays about: "Context Aware Recommendation"

Showing result 1 - 5 of 8 essays containing the words Context Aware Recommendation.

1. 1. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers

   University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

   Author : Edda Waciira; Marah Thomas; [2023]
   Keywords : e-commerce; Fashion Recommendation Systems; Machine Learning; Recommendation Systems;

   Abstract : In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. READ MORE

3. 2. Generating personalized music playlists based on desired mood and individual listening data

   University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Author : Jennifer Svensson; [2023]
   Keywords : music recommendation; context-based music listening; mood regulation; affect regulation; Spotify Audio Features; music-mood classification; context-aware music recommendation;

   Abstract : Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. READ MORE

4. 3. Code Reviewer Recommendation : A Context-Aware Hybrid Approach

   University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Author : Anton Strand; Markus Gunnarsson; [2019]
   Keywords : Code review; Context-aware; Recommender Systems; Gerrit;

   Abstract : Background. Code reviewing is a commonly used practice in software development. It refers to the process of reviewing new code changes, commonly before they aremerged with the code base. However, in order to perform the review, developers need to be assigned to that task. READ MORE

5. 4. Stay active. : Factors motivating elderly people to stay physically active after physiotherapy

   University essay from Luleå tekniska universitet/Hälsa och rehabilitering

   Author : Ahmed El Shafey; [2019]
   Keywords : Physical activity; older people; motivation; physical therapy.;

   Abstract : Background: Despite the known benefits of physical activities in the management of many chronic diseases associated with aging, a majority of elderly patients within primary health care have difficulties reach the daily recommendation of physical activity and risking being inactive after physiotherapy. Therefore, it is important to understand the factors influencing their motivation in order to provide support for them to stay physically active after physiotherapy. READ MORE

6. 5. Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation

   University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

   Author : Oktay Bahceci; [2017]
   Keywords : Information Filtering; Information Retrieval; Search Engine; Search Engines; Recommendation; Music Recommendation; Personalized Recommendation; Personalised Recommendation; Context Aware Recommendation; Recommender Systems; Statistical Learning; Artificial Intelligence; Machine Learning; Deep Learning; Neural Networks; Artificial Neural Networks; Feed Forward Neural Networks; Convolutional Neural Networks; Recurrent Neural Networks; Deep Neural Networks; Embedding;

   Abstract : Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. READ MORE