An AI-based solution for the cold start and data sparsity problems in the recommendation systems
In recent years, the amount of data available on the internet and the number of users who utilize the Internet have increased at an unparalleled pace. The exponential development in the quantity of digital information accessible and the number of Internet users has created the possibility for information overload, impeding fast access to items of interest on the Internet. Information retrieval systems like as Google, DevilFinder, and Altavista have partly overcome this challenge, but prioritizing and customization of information (where a system maps accessible material to a user's interests and preferences) were lacking. This has resulted in a higher-than-ever need for recommender systems. Recommender systems are information filtering systems that address the issue of information overload by filtering important information fragments from a huge volume of dynamically produced data based on the user's interests, favorite things, preferences and ratings on the desired item. Recommender systems can figure out if a person would like an item or not based on their profile.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalRecommendation SystemsRetrievalSimilar Papers 제목 키워드 기반
DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems
Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the p…
Recommendation SystemsDeep Pairwise Hashing for Cold-start Recommendation
Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy i…
DenoisingMARec: Metadata Alignment for cold-start Recommendation
For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks…
Collaborative FilteringRecommendation SystemsA Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models are based on the probabilistic graphical…
graph constructionmodelDivide-and-Conquer: Cold-Start Bundle Recommendation via Mixture of Diffusion Experts
Cold-start bundle recommendation focuses on modeling new bundles with insufficient information to provide recommendations. Advanced bundle recommendation models usually learn bundle representations from multiple views (e…
Mixture-of-Experts