A Novel Approach Towards Context Based Recommendations Using Support Vector Machine Methodology

Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

“…In (11), (13), and (15), the trend fuzzy relations are tuned for each content category. In (12) and (14), the trend fuzzy relations are tuned regardless of the genre. The results of the primary fuzzy model tuning are presented in Appendix A. Parameters of the membership functions for the input and output primary fuzzy terms are presented in Tables A1 and A2.…”

“…In neural computing, the rule mining problem is solved using the traditional "find all-then prune and select" approach [12,13]. Rule generation using the support vector machine methodology provides enhanced recommendation accuracy [14]. To identify fine-grained time and content preferences, a support vector machine (SVM) separates and classifies the data via hyperboxes.…”

Recommendation and Classification Systems: A Systematic Mapping Study

“…e search engines returned multiple results (Table 4), with a total of 21 proposals remaining were potential candidates. A smart-device news recommendation technology based on the user click behavior [44] Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach [45] A novel approach towards context based recommendations using support vector machine methodology [46] A smartphone-based activity-aware system for music streaming recommendation [47] An app usage recommender system: improving prediction accuracy for both warm and cold start users [48] Proposing design recommendations for an intelligent recommender system logging stress [49] A recommender system based on implicit feedback for selective dissemination of eBooks [50] A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases [51] An approach to content based recommender systems using decision list based classification with k-DNF rule set [52] Probabilistic approach for QoS-aware recommender system for trustworthy web service selection [53] Approach to cold-start problem in recommender systems in the context of web-based education [54] Context and intention-awareness in POIs recommender systems [55] A collaborative filtering-based re-ranking strategy for search in digital libraries [56] Learning users' interests by quality classification in market-based recommender systems [57] Mobile content recommendation system for revisiting user using content-based filtering and clientside user profile [58] A hybrid collaborative filtering algorithm based on KNN and gradient boosting [59] A scalable collaborative filtering algorithm based on localized preference [60] Recommended or not recommended? Review classification through opinion extraction [61] Meta-feature based data mining service selection and recommendation using machine learning models [62] Personalized channel recommendation deep learning from a switch sequence [63] Affective labeling in a content-based recommender system for images [64] A novel approach towards context sensitive recommendations based on machine learning methodology [65] A distance-based approach for action recommendation [66] Ranking and classifying attractiveness of photos in folksonomies…”

Recommender Systems for Driver Information Support: State-of-the-Art Review

“…1. Количество найденных публикаций в области информационной поддержки водителя Несмотря на схожую тематику представленных работ, список проблем, покрываемых ими, крайне неоднороден -одни статьи сфокусированы на сугубо теоретической проблематике, без касательства к вероятной практической области применения (например, работы, оценивающие эффективность разных математических моделей, используемых для создания рекомендаций [5][6][7][8]), иные же, наоборот, посвящены решению весьма прикладных задач, возникающих в ходе проектирования конкретного технического решения (например, MOVE [4], где описывается проблема восприятия водителем информации, представленной в различных текстово-графических формах). Подобное многообразие отражает сложность и глубину исследуемой темы.…”