Essays about: "Recommender Systems Evaluation"

Showing result 1 - 5 of 29 essays containing the words Recommender Systems Evaluation.

  1. 1. Designing Diverse Features to Reduce the Filter Bubble Effect on Social Media

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ramya Kandula; [2023]
    Keywords : Filter bubbles; recommender systems; diversity; social media; filter bubblor; rekommenderande system; mångfald; sociala medier;

    Abstract : The filter bubble effect has been an active area of research that has been explored in various contexts within social media. Research on recommender system designs within filter bubbles has received a lot of attention, mainly due to its impact on the phenomena. READ MORE

  3. 2. Shoppin’ in the Rain : An Evaluation of the Usefulness of Weather-Based Features for an ML Ranking Model in the Setting of Children’s Clothing Online Retailing

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Isac Lorentz; [2023]
    Keywords : Statistical analysis; regression analysis; recommender systems; ensemble learning; electronic commerce; LightGBM; learning to rank; feature selection; weather-based features; fashion; Statistisk analys; regressionsanalys; rekommendationssystem; ensemble-inlärning; näthandel; LightGBM; learning to rank; variabelselektion; väderbaserade variabler; mode;

    Abstract : Online shopping offers numerous benefits, but large product catalogs make it difficult for shoppers to understand the existence and characteristics of every item for sale. To simplify the decision-making process, online retailers use ranking models to recommend products relevant to each individual user. READ MORE

  4. 3. Developing Machine Learning-based Recommender System on Movie Genres Using KNN

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

    Author : Anthony Ezeh; [2023]
    Keywords : : Movie Recommender System; Machine Learning; Content-based Filtering; Collaborative Filtering; KNN Algorithms; Classification Algorithm;

    Abstract : 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

  5. 4. Attention-based Multi-Behavior Sequential Network for E-commerce Recommendation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Zilong Li; [2022]
    Keywords : Recommendation System; Sequential Recommendation; Click-through Rate Model; Transformer; Multi-Task Learning; Sistema di Raccomandazione; Raccomandazione Sequenziale; Modello di Percentuale di Clic; Trasformatore; Apprendimento Multitasking; Rekommendationssystem; Sekventiell rekommendation; Klickfrekvensmodell; Transformator; Multi-Task Learning;

    Abstract : The original intention of the recommender system is to solve the problem of information explosion, hoping to help users find the content they need more efficiently. In an e-commerce platform, users typically interact with items that they are interested in or need in a variety of ways. For example, buying, browsing details, etc. READ MORE

  6. 5. Using Recommendation Engines to Improve the userexperience in a Cloud Analytic Solution

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Muhammad Ishfaq; [2022]
    Keywords : ;

    Abstract : Recommender systems have become an integral part of everyday human life because of tworeasons: addressing the issue of information filtering in the world of big data which limits therecommendation engine’s capabilities, and improving user experience by helping the userswhat users want. It is very often that users are unable to find preferred results with a simplequery or are unaware that even if it exits. READ MORE