Essays about: "cold-start"
Showing result 1 - 5 of 23 essays containing the word cold-start.
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1. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : 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
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2. Mitigating serverless cold starts through predicting computational resource demand : Predicting function invocations based on real-time user navigation
University essay from Jönköping University/JTH, Avdelningen för datateknik och informatikAbstract : Serverless functions have emerged as a prominent paradigm in software deployment, providing automated resource scaling, resulting in demand-based operational expenses. One of the most significant challenges associated with serverless functionsis the cold start delay, preventing organisations with latency-critical web applications from adopting a serverless technology. READ MORE
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3. Developing Machine Learning-based Recommender System on Movie Genres Using KNN
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : With an overwhelming number of movies available globally, it can be a daunting task for users to find movies that cater to their individual preferences. The vast selection can often leave people feeling overwhelmed, making it challenging to pick a suitable movie. READ MORE
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4. Evaluating Cold-Start in Recommendation Systems Using a Hybrid Model Based on Factorization Machines and SBERT Embeddings
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The item cold-start problem, which describes the difficulty of recommendation systems in recommending new items to users, remains a great challenge for recommendation systems that rely on past user-item interaction data. A popular technique in the current research surrounding the cold-start problem is the use of hybrid models that combine two or more recommendation strategies that may contribute with their individual advantages. READ MORE
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5. Regularized Fine-tuning Strategies for Neural Language Models : Application of entropy regularization on GPT-2
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : Deep neural language models like GPT-2 is undoubtedly strong at text generation, but often requires special decoding strategies to prevent producing degenerate output - namely repetition. The use of maximum likelihood training objective results in a peaked probability distribution, leading to the over-confidence of neural networks. READ MORE
