Evaluating Design Choices in Tripartite Graph-Based Recommender Systems to Improve Long Tail Recommendations

Even though the abaility to recommend items in the long tail is one of the main strengths of recommendation systems, modern models still show decreased performance when recommending these niche items. Various bipartite and tripartite graph-based models have been proposed that are specifically tailored to solving this long tail issue. This study...

Adapting to Dynamic User Preferences in Recommendation Systems via Deep Reinforcement Learning

Recommender Systems play a significant part in filtering and efficiently prioritizing relevant information to alleviate the information overload problem and maximize user engagement. Traditional recommender systems employ a static approach towards learning the user's preferences, relying on logged previous interactions with the system,...

Alleviating the cold-start problem by using demographic data and domain-aware similarity measure

Recommender systems (RS) are a cornerstone for most online businesses that cater to a large customer base such as e-commerce, social network platforms and many others. RS's enable these platforms to provide tailor-made experiences to each of their customers by strategically utilizing users/items rating data or any other available data....

Minimizing the Long-tail Problem in Collaborative Filtering Based Recommender Systems Using Clustering

Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings...