Results 11 to 20 of about 7,528,176 (292)
The Continuous Cold Start Problem in e-Commerce Recommender Systems [PDF]
CoRR, 2015 Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this `cold-start problem' have been proposed in the literature.
Bernardi, L. +3 more
core +14 more sources
Addressing the Extreme Cold-Start Problem in Group Recommendation
CoRR, 2022 The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice.
Linxin Guo +5 more
openaire +4 more sources
DotMat: Solving Cold-Start Problem and Alleviating Sparsity Problem for Recommender Systems
2022 IEEE 5th International Conference on Electronics Technology (ICET), 2022 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 problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge.
Wang, Hao
openaire +4 more sources
Information, 2022 Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items.
Julio Herce-Zelaya +4 more
doaj +1 more source
Algorithms, 2022 A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads.
Iosif Viktoratos, Athanasios Tsadiras
doaj +1 more source
Normalization-based neighborhood model for cold start problem in recommendation system [PDF]
, 2020 Existing approaches for Recommendation Systems (RS) are mainly based on users’ past knowledge and the more popular techniques such as the neighborhood models focus on finding similar users in making recommendations.
Zahid, Aafaq +2 more
core +1 more source
IEEE Access, 2022 The cold-start problem is one of the main challenges in recommender systems and specifically in collaborative filtering methods. Such methods, albeit effective, typically can not handle new items or users that do not have any prior interaction activity ...
Alireza Gharahighehi +2 more
doaj +1 more source
Enhancing Traditional Recommender Systems via Social Communities [PDF]
Vietnam Journal of Computer Science, 2019 Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no ...
Leschek Homann +3 more
doaj +1 more source
RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User
IEEE Access, 2020 Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce ...
Yaru Jin +3 more
doaj +1 more source
Hybrid Deep Framework for Group Event Recommendation
IEEE Access, 2020 Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed.
Ruichang Li +3 more
doaj +1 more source